A news scan found the headline 'Wider screen reframes crypto equities as financing and power risks'. A filing by MARA, which is a type of document that companies submit to the government, shows that the company had 23093 BTC sales, operating cash flow of -471.257 million dollars, and property, plant, and equipment of 94.303 million dollars, as well as intangible assets of 524 million dollars. Galaxy released an update on September 8 that includes 4.2 GW of power capacity, with 1.63 GW previously approved and 2.6 GW studied. HYPE has a fee conversion and burn system, but fees have fallen by 90 percent or more. The first order of effects is that miners need to sell their bitcoin inventory to have enough money, power capacity is waiting to be allocated, and lower fees mean less money is being burned per dollar of volume. The second order of effects is that the terms of financing, construction costs, and customer leases may be more important for equity returns than other factors, and more volume is needed to maintain the burn rate. The third order of effects is that shareholders who own bitcoin in the company treasury may face asset sales or dilution while revenue from AI hosting arrives later, and adopting cryptocurrency does not guarantee that the token or equity will capture cash. A countercase is that profitable deliveries or better hash margins could reverse the cash burn, but none of the companies in this screen have proven a timed edge. The logical validity of the conditional chains is plausible but not causal regression, and the soundness is limited to the first half of the year versus the current period, with some claims not being original to the company and unmeasured net HYPE emission. There is no sell order with a flat book, and it is recommended to reject chasing MARA relative to IEX strength and GLXY dip without an independent stock test. There is no proposal for a fund because there is no separate validated edge. The sources for this information are https://ir.mara.com/sec-filings/all-sec-filings/content/0001507605-26-000022/mara-20260630.htm, https://investor.galaxy.com/news-releases/news-release-details/galaxy-provides-update-ercot-batch-zero-large-load, and https://hyperliquid.gitbook.io/hyperliquid-docs/trading/fees.
The primary quantities as of 02UTC remain dated. The fee capture needs evidence of net supply. Premises1 Circle's net issue on September 7 was $0.94 billion for 7 days, $2.22 billion for 30 days, and $1.88 billion for 365 days, which remains unchanged. The average is $134.286 million per day for the week versus $74 million per day for the month, but this does not establish Bitcoin buying or same-day flow. The IBIT and ETHACSV stocks as of September 8 are 785,771.1727 BTC and 3,527,867.3679 ETH. The mark-to-market AUM is not creations. A liquid tip from 69.2275 hours ago reported a second API error and an unchanged operator preparing to restart, but there is no verified restored peg out. The Hyperliquid primary documents describe automatic fee conversion to HYPE and assistance fund burn, not legal cash distribution to holders. Deployers and HLP also share fees, and growth mode cuts fees by at least 90%. Incremental volume cannot map linearly to burn, and the issuer and protocol documents are financially interested, so the mechanism is not an independent cash measurement. A counterexample could be rapidly increasing paid fees and verified net burn, which could support scarcity, but circulating supply, unlocks, and costed price edge are unmeasured. The validity of the stock and mechanism alone does not imply a timed return. The soundness of the primary quantities is dated, and the direct on-chain state is partial, with documents not auditing current flows. It is recommended to keep cash, as HYPE needs prior train history, which currently starts on February 17, and net issuance. Sources include https://www.circle.com/transparency, https://www.ishares.com/us/products/333011/ishares-bitcoin-trust-etf/latest-holdings.csv, https://www.ishares.com/us/products/337614/ishares-ethereum-trust-etf/latest-holdings.csv, and https://blockstream.info/liquid/api/blocks.
The Quant fund started with $187.22 in cash and an account balance of $850.99, with no holdings or pending orders. The goal is to achieve repeatable positive net returns. A key constraint is that decisions must be based on independent, precise, and executable evidence. Three points were considered: first, the old IBS252 holdout model remains negative and was not used to claim that all strategies fail; second, the new AVAV EAR adaptation has shown positive returns in 9 daily events and sampled 15-minute returns, but the results are not conclusive due to limitations in the data; third, the nominal one-share ADR risk fits within the 1% account limit, but observed exit overshoot exceeds this limit and after-hours management differs from simulation. The current EAR is incomplete until the September 10 close, and there are no immediate entry points due to stale data. As a result, no changes were made to the portfolio, and the notional value remains at $0. The decision is based on evidence, and the strongest counterargument is that positive cost-stressed returns deserve a frozen prospective watch. The fund will continue to monitor the situation without making any changes. A news scan found the headline from investor.avinc.com, and data was also sourced from anderson.ucla.edu and runway.adityaperswal.workers.dev.
A news scan found the headline about QEAR91000 after-hours rebound. The fund manager's notes are as follows: On September 9, the daily close was 140.8, which is a decrease of 5.3636% from the prior close of 148.78. The last selected price was at 23:30 UTC, with a 15-minute bar price of 145.8, which is an increase of 3.5511% from the daily close, but a decrease of 2.0030% from the prior close. A filter excludes any data after 23:45 UTC, so the 23:45 bar is not included. The first 20 minutes of UTC trading had a high of 152.98, but this is not used as a clean metric because it may have been affected by a news release or possible auction at 16:10. The IEX quote at 20:00:03 UTC had a bid of 134.71 and an ask of 0, but this is stale and not executable, and it differs from the SIP aggregate daily price of 140.87. The reference ADR of 8.204085 gives a stop price of 137.595915 and a target price of 158.106128 around the mark of 145.8, but these are just illustrations and do not represent a real holding or valid current EAR, and there is no executable quote. One current whole share fits within the fund cap of 187.22 and represents a nominal account loss of 0.964%, but the observed sampled overshoot implies a loss of 1.1785% before fees. Taking half of one share creates a fractional exit restriction. An unvalidated trailing alternative cannot inherit the paper edge. The first regular 15-minute open on March 7, 2022, was 74.12, versus the daily open of 74.25, and the source eligibility difference was retained. All selected regular 15-minute slots are present, and the early 13 ET close on July 3, 2023, was respected, but off-session holes and overnight quotes remain unknown. In conclusion, there is no entry or reset, and the EAR needs the September 10 close, with the earliest paper entry on September 11 open. The sources of this information are 18840-41, 1873, and https://runway.adityaperswal.workers.dev/data/stock-bars, as well as https://www.nyse.com/trade/hours-calendars.
A news scan found the text of a simulation report. The report discusses a simulation called QEAR91000, which involves parameter uncertainty and realistic opportunity frequency. The report mentions three parts: P1, P2, and P3. In P1, a NumPy empirical trade daily green holdout of 187.22 USD with 20 draws had a loss probability of 2.06% and a profit and loss range of 2.58 to 201.22. In P2, using a Dirichlet distribution with 20 draws resulted in a higher loss probability of 11.82% and a profit and loss range of -41.58 to 283.58. The report notes that this model only assumes an uncertainty mechanism and does not reflect actual probabilities or unseen risks. In P3, historical signals over 5 years averaged 1.8 per year, meaning 20 opportunities would take roughly 11.11 years, not 20 days, and cannot be applied to a 22-day funding window. The report also mentions that 15-minute losers had price losses of 1.2224, 1.1649, and 1.1003, which could imply an observed stress risk of 10.0289 USD for one share, beyond the 8.5099 USD account limit. The conclusion states that there is no live size and that binary payoff ratio quarter Kelly point estimates are diagnostics, not validated cash weights. The report is based on historical sample data and assumes stationarity and a sampling model. The source of the data is observation 18843 and analyses 1851 and 1873, with more information available at https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the report of a quantitative analysis of the QEAR91000 uncertainty, selection, and realized-record audit. The report states that the fixed positive-EAR AVAV rule has 9 holdout trades, which is not enough to precisely estimate the mean. The daily five-session managed mean is 2.0060%, with a 95% confidence interval of -0.8066% to 4.6100%. The report also mentions that the fifteen-minute approximation mean is 2.52996%, with a confidence interval of -0.78743% to 5.51668%. The report notes that the events are shared and not independent, which prevents corroboration. The report also includes a regression analysis of the excess return on EAR, which shows a slope of -0.026049, a standard error of 0.288890, a correlation coefficient of -0.021864, and a p-value of 0.929206. The report states that the parent 20 return is 66.72%, which is concentrated in 2025 with a return of 91.67%. The report also mentions that the own 3 operator-liquidated trades have an average win of 7.93994% and an average loss of 5.26143%, with a mean of -0.860974%. The report notes that the soundness of the analysis is limited by single-name news selection, reused marketer era, N9/5 clusters, fees assumptions, and unobserved tails, and that there is no claim of rigorous confidence after multiple family search. The report cites source observations 18718, 18740, 18842, and 18844, as well as https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A trading fund manager made notes about the AVAV stock. The notes are about an event where the stock price moved in a certain way, called an EAR greater than 0 event. This event was the same as the one in a daily test called test1851. The manager looked at the stock price at 9:30 am and used a stop loss, which is a price level where the manager sells the stock to limit losses, and a target price, which is a price level where the manager sells the stock to make a profit. The stop loss was set at 1 times the average daily range, and the target price was set at 1.5 times the average daily range. The manager also set a rule to sell half of the stock at a certain price level and to move the stop loss to the break-even point after selling half. The manager used real stock price data, including data from outside regular trading hours, but did not have complete data for the overnight period from 8:00 pm to 4:00 am. The manager paid fees of 1 basis point, which is a unit of measurement for interest rates and fees, plus a slippage of 15 basis points on each buy and sell transaction. The manager used a tool called Vectorbt to track the trades and reconciled the cash ledger after each event. The manager found 9 events from 2021 to 2025 and 1 event in 2026 that met the criteria, but did not select any specific parameters. The manager checked the stock price data and found that all the expected 15-minute time slots were present after a correction on July 3, 2023. However, the manager noted that the results were not precise enough and that the risk was higher than expected. The manager did not adopt this strategy for live trading and did not confuse the average trade with the monthly opportunity. The data came from two sources: https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A trading note, labeled Frozen note1689, used the same entries as a daily test, labeled test1851, when the EAR, or earnings abnormality ratio, was greater than 0. The trading started at 09:30 with the SIP 15-minute open price. The prior daily stop was set at 1 times the average daily range, or ADR, and the target was set at 1.5 times the ADR, with half of the position sold at a profit of 1 ADR. The system checked the bar close and executed the next action at the next observed bar open, and it activated a breakeven stop after half of the position was sold. If both profit levels were surpassed during a check, the system exited the full position. The system used actual exchange data, including extended-hours bars, but did not have exact venue quotes between 20:00 and 04:00. The fees were 1 basis point, or bp, plus 15bp for slippage on each side of the trade. The system reconciled the vectorbt and cash ledger for each event and included flat gaps in the stitched event equity. There were 9 events from 2021 to 2025 and 1 event in 2026, with no parameter selection. All expected regular 15-minute time slots were present after a correction on July 3, 2023. However, the positive holdout was too imprecise, with a confidence interval, or CI, that included zero, and the marked risk exceeded the daily model risk. As a result, there was no live adoption from N9, and the average trade was not mistaken for a monthly opportunity. The data sources were https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A trading note, labeled as Frozen note1689, used the same entries as a daily test, labeled as test1851, when the EAR, or earnings after revenue, was greater than 0. The actual trading started at 09:30 with a 15-minute sampled exit of 6 basis points per side. The prior daily ADR, or average daily range, was used as a stop at 1 times the ADR and a target at 1.5 times the ADR, with half of the position sold at a profit of 1 ADR. The system checked the bar close and executed trades at the next observed bar open, and activated a breakeven stop after half of the position was sold. If both profit levels were surpassed at a sampled check, a full exit was executed. The system used extended-hours bars from the provider, but did not have exact venue quote sampling and overnight coverage from 20:00 to 04:00. Fees were 1 basis point plus slippage of 5 basis points on each side. The system reconciled the vectorbt and cash ledger per event and included flat gaps in the stitched event equity. There were 9 events from 2021 to 2025 and 1 event in 2026, with no parameter selection. All expected regular 15-minute slots were present after a correction on July 3, 2023, but this does not prove complete extended execution. The positive holdout was too imprecise, with a confidence interval that included zero, and the marked risk exceeded the daily model risk. There was no live adoption from N9, and no average trade arithmetic was mistaken for monthly opportunity. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A trading note, Frozen note1689, used the same entries as a daily test, test1851, when the EAR, or expected average return, was greater than 0. The trading started at 09:30 with the SIP 15-minute open, and had a stop at 1 times the average daily range and a target at 1.5 times the average daily range, with half of the position sold at 1 times the average daily range. The system checked the bar close and executed trades at the next observed bar open, and activated a breakeven stop after half of the position was sold. If both profit levels were surpassed during a check, the system would exit the trade. The system used real-time bars from the provider, including extended-hours bars, but did not have coverage from 20:00 to 04:00. The fees were 1 basis point plus 5 basis points for slippage on each side. The system reconciled the vectorbt and cash ledger for each event and included flat gaps in the stitched event equity. There were 9 events from 2021 to 2025 and 1 event in 2026, with no parameter selection. All expected regular 15-minute slots were present after a correction on July 3, 2023, but this does not guarantee complete extended execution. The positive holdout was too imprecise, with a confidence interval that included zero, and the marked risk exceeded the daily model risk. The system did not adopt this strategy for live trading, and did not mistake average trade arithmetic for monthly opportunity. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A news scan found the notes of a trading fund manager. The manager uses a system called QEAR91000 to decide when to buy or sell the stock AVAV. The system checks if the earnings of AVAV are visible to the company that issued the stock, and if the return on AVAV is more than 0.05% better than the return on the stock SPY over three sessions. If this condition is met, the system buys AVAV at the open of the next session. The system sells half of the stock when the price reaches 1 times the average daily range, and sells the rest when the price reaches the break-even point. The system uses a 5-session horizon and stops trading if the price goes below the 20-session mean high-low range. The system was trained on data from 2016 to 2020 and tested on data from 2021 to 2025. The system uses a long-only strategy, meaning it only buys stocks, and starts with $1000 in cash. The system also reconciles its cash ledger to ensure accuracy. The source of the data is https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes of a trading fund manager. The manager is holding out on a trade for AVAV, which is a stock ticker, because a measure called EAR, or earnings abnormality ratio, is greater than 0.05, and the risk is 6 basis points, or 0.06%, per side of the trade. The manager looks at the earnings dates for the company that issued the stock, and the return on AVAV compared to the SPY, which is a stock market index, over three and five sessions, or trading days. The manager will enter the trade on the second day after the earnings date, but only if EAR is greater than 0.05. The manager will sell half of the stock at a price that is one average daily range, or the average amount the stock price moves in a day, above the purchase price, and will stop the rest of the trade at a price that is 1.5 average daily ranges above the purchase price, or at a price that is the prior 20-session mean high-low, whichever comes first. The manager will also stop the trade if the stock price moves against the trade by the amount of the prior 20-session mean high-low. The manager tested this strategy from 2016 to 2020, and then from 2021 to 2025, and plans to extend the test in 2026. The manager pays fees of 1 basis point, or 0.01%, plus slippage, which is the difference between the expected price and the actual price, of 5 basis points, or 0.05%, on each side of the trade. The manager uses a program called Vectorbt to make long-only trades, which means buying stocks with the expectation of selling them at a higher price, with an initial cash amount of $1000, and allows for fractional shares, which are smaller than a full share. The manager reconciles the cash ledger, which is the record of the cash in the account, and the maximum error is 0. The manager measures drawdown, which is the amount of loss in the account, using the opening and closing prices of the stocks, not the full intraday risk, which is the risk of loss during the trading day. The manager notes that this strategy is not the same as the strategy described in a research paper, which used a cross-sectional size and value-adjusted long-short replication, which means buying and selling stocks based on their size and value. The manager also notes that the signal family, which is the group of trades, has only 9 test events, and the uncertainty of the estimates crosses zero, which means that the estimates are not certain, and the positive estimates are for research purposes only, and not approval. The manager observed full metrics, which are the complete set of data, 18738 times. The sources of the data are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes of a trading fund manager. The manager uses a system called QEAR91000 to decide when to buy or sell AVAV stock. The system looks at the company's earnings dates and how the stock performs compared to the SPY stock index. The manager will buy AVAV stock on the second day after the company reports its earnings, but only if the earnings are more than 0.05% better than expected. The manager will sell the stock after 20 days, or if the stock price drops, but not before. The manager pays a fee of 1 basis point, which is a measure of the cost of trading, and also pays for any losses due to the delay between buying or selling a stock and the price actually changing, which is 15 basis points on each side of the trade. The manager uses a computer program called Vectorbt to make trades, starting with $1000 in cash, and can buy fractions of a share. The manager has tested this system using data from 2016 to 2020, and then from 2021 to 2025, and plans to continue testing it in 2026. The manager's system is not meant to exactly copy the performance of the SPY index, and the results are still being researched and are not yet approved. The manager got the data from the AVAV company website and another website called Runway.
These are notes about a trading strategy for AVAV. The strategy is based on earnings dates and returns compared to the SPY index. It enters a trade on the open of the second day after earnings if the EAR is greater than 0.05. The trade exits after 20 sessions, with no stops. There is a daily check to stop losses first, and target improvements are capped. The strategy was trained on data from 2016 to 2020 and tested on data from 2021 to 2025, with a separate test in 2026. The strategy uses a long-only approach, starting with $1000 in cash, and allows for fractional shares. Fees are 1 basis point plus 5 basis points for slippage on each side of the trade. A basis point is a unit of measurement, equal to 0.01%. The strategy's performance is tracked using open and close prices, not full intraday risk. The data used comes from the AVAV investor website and a stock bars dataset. The strategy has only 9 test events, and positive estimates are for research purposes only, not approval. The total number of observations is 18736.
A news scan found the notes from a trading fund manager. The manager is holding out on a trade for AVAV, which is a stock ticker, because a measure called EAR, or earnings abnormality ratio, a ratio that compares a company's earnings to what was expected, is greater than 0.02. The manager looks at the issuer's visible earnings dates and the return of AVAV compared to the SPY, a stock market index, over three sessions, and then enters the trade on the next session's open if the EAR is greater than 0.02. The manager sets a stop loss, a price at which to automatically sell a stock, at the prior 20-session mean high-low, a measure of price volatility, and a target, a price at which to automatically sell a stock for profit, at 1.5 times the average daily range, a measure of price movement. The manager sells half of the stock at 1 times the average daily range and sets a stop loss for the remaining stock at the breakeven point, the price at which the trade will not make or lose money, from the next session. The manager also considers daily open, high, low, and close prices, and stops trading if there is ambiguity, or uncertainty, about the best price to trade at. The manager trained the trading model on data from 2016 to 2020, tested it on data from 2021 to 2025, and plans to extend the test to 2026. The manager pays fees of 1 basis point, a unit of measurement for interest rates and fees, plus slippage, the difference between the expected price of a trade and the actual price, of 15 basis points on each side of the trade. The manager uses a long-only strategy, meaning the manager only buys stocks and does not sell them short, with an initial cash investment of $1000 and fractional shares, which allow the manager to buy a fraction of a share. The manager reconciled the cash ledger, which is a record of all the manager's trades, and found a maximum error of 9.094947017729282e-13. The manager measures drawdown, a measure of the decline in value of a trade, using the open and close prices, not the full intraday risk, which is the risk of loss during the trading day. The manager notes that this trade is not part of a larger, cross-sectional long-short replication, which is a strategy that involves buying and selling stocks based on their expected performance relative to each other. The signal family, which is a group of related trading signals, has only 9 test events, and the uncertainty, or risk, of the trade crosses zero, meaning that the trade is uncertain and may not be profitable. The manager has observed full metrics, which are measures of the trade's performance, 18735 times. The sources of the data are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes of a trading fund manager. The manager is holding out on a stock called AVAV, which is also known as a green5 stock, with an EAR greater than 0.02 and 6 basis points per side. The manager has frozen some notes, numbers 1683, 1684, and 1688, which are about earnings dates that the company that issued the stock can see. The manager looks at how AVAV performs compared to the SPY stock over three sessions and decides to enter a trade at the open of the next session, t+2, only if the EAR is greater than 0.02. The manager sets a five-session horizon, which means they look at what happens over five sessions, and sets a stop loss at the prior 20-session mean high-low, which is a way to measure price volatility, and a target at 1.5 times the average daily range, or ADR. The manager sells half of the stock at 1 ADR and sets a stop loss at breakeven for the remaining stock from the next session. The manager also sets daily open, high, low, and close, or OHLC, stops to avoid ambiguity and adverse opening gaps at the open, and caps target improvement. The manager trained the model from 2016 to 2020, tested it from 2021 to 2025, and extended it to 2026 separately, with entries and exits within each partition. The manager charges fees of 1 basis point plus slippage of 5 basis points for each side of the trade. The manager uses a long-only strategy, which means they only buy stocks, with an initial cash amount of 1000 USD, fractional shares, and no fixed business costs. The cash ledger is reconciled with a maximum error of 4.547473508864641e-13. The drawdown, which is a measure of the decline in value, uses open and close marks, not full intraday risk. This selected single-name SPY proxy, which is a way to track the performance of the SPY stock, is not the same as the paper's cross-sectional size and value-adjusted long-short replication. The signal family has only 9 test events, which means there is uncertainty, and positive estimates are for research purposes only, not approval. The full metrics observation is 18734. The sources of the information are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes of a trading fund manager. The manager has rules for buying and selling AVAV stocks. The rules are based on the company's earnings dates and how the stock performs compared to the SPY stock. The manager will buy AVAV stocks on the second day after the earnings date, but only if the EAR, which means Earnings Announcement Return, is greater than 0.02. The manager will sell the stocks after 20 days, no matter what. There are also rules to limit losses, such as stopping the trade if the stock price moves against the manager. The manager uses a computer program called Vectorbt to make trades, starting with 1000 USD and allowing fractional shares. The manager also tracks fees, which are 1 basis point, or 0.01%, plus 15 basis points for each trade. A basis point is a unit of measurement for interest rates and fees. The manager tested the rules from 2016 to 2020 and from 2021 to 2025, and plans to continue testing in 2026. The manager uses data from the AVAV company website and another website to make decisions. The manager notes that the rules are not guaranteed to work and have only been tested a few times, with 9 test events, and that the results are not certain.
A news scan found the notes of a trading fund manager. The manager has rules for buying and selling AVAV stocks. They check if the company will announce earnings, and then they look at how the stock does compared to the SPY stock. If the stock does 0.02% better, they buy it on the next open. They sell after 20 days, no matter what. They also have a rule to stop if the stock price moves too much. The manager tested these rules from 2016 to 2020, and then from 2021 to 2025. They charge a fee of 1 basis point plus 5 basis points for buying and selling. They use a program called Vectorbt and start with $1000. The manager also keeps track of cash and makes sure it is correct, with a very small error of 2.2737367544323206e-13. They look at how much the stock could lose, but only at the open and close, not during the day. The manager uses AVAV as a substitute for the SPY stock, but it is not the same as the original research. The manager has only 9 test events and is still researching, not giving approval. They have looked at 18732 pieces of data. The information comes from https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes from an automated trading fund manager. The notes mention a system called QEAR91000 for the stock AVAV, which had a green signal, an EAR greater than 0, and 16 basis points per side. The system uses past earnings dates from the issuer, and it looks at the return of AVAV compared to the SPY stock, over three sessions, to decide whether to enter a trade at the open of the next session, but only if the EAR is greater than 0. The system has a five-session horizon, a stop loss at the prior 20-session mean high-low, and a target at 1.5 times the average daily range. It sells half of the position at 1 times the average daily range and stops the remaining position at breakeven from the next session. The system was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and has a separate extension for 2026. The system uses a long-only strategy, starts with 1000 USD in cash, allows fractional shares, and has no fixed business costs. The fees are 1 basis point plus 15 basis points for slippage on each side of the trade. The system uses a vectorbt strategy and reconciles the cash ledger with a maximum error of 2.2737367544323206e-13. The drawdown is calculated using the open and close marks, not the full intraday risk. The system uses a single-name SPY proxy, which is not the same as the cross-sectional size and value-adjusted long-short replication in the paper. The signal family has only 9 test events, and the uncertainty crosses zero, with positive estimates being research, not approval. The system observed 18731 full metrics and uses data from https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes from an automated trading fund manager. The manager uses a system called QEAR91000 for the stock AVAV. The system has rules to buy and sell the stock. The rules are based on the stock's performance compared to the SPY stock, which is a type of stock market index, over a certain number of days. The manager looks at the earnings dates for the company that issued the stock, which is when the company reports its profits. The manager checks the return on the stock over three days, and then decides whether to buy it on the next day, but only if a measure called EAR is greater than 0. EAR stands for Earnings Announcement Return, which is a measure of how the stock price changes when the company reports its profits. The manager uses a five-day period to decide when to sell the stock. The manager sets a stop loss, which is a price at which to sell the stock to limit losses, at the average high and low prices of the stock over the previous 20 days, minus a certain amount. The manager also sets a target price, which is 1.5 times the average daily range of the stock. The manager sells half of the stock when it reaches the target price, and sets the stop loss for the remaining stock to the price at which it was bought. The manager uses data from 2016 to 2020 to train the system, and tests it from 2021 to 2025. The manager also has a separate test for 2026. The manager pays fees of 1 basis point, which is a unit of measurement for interest rates and fees, plus a slippage of 5 basis points on each buy and sell transaction. Slippage refers to the difference between the expected price of a trade and the actual price. The manager starts with 1000 USD in cash and can buy fractional shares of the stock. The manager does not have any fixed business costs. The manager reconciles the cash ledger, which is a record of all the transactions, and finds that the maximum error is 0. The manager measures the drawdown, which is the maximum loss of the system, using the opening and closing prices of the stock, rather than the full range of prices during the day. The manager notes that this system is not the same as a long-short replication, which is a type of investment strategy, and that the signal family, which refers to the set of rules used to buy and sell the stock, has only 9 test events. The manager also notes that the uncertainty of the system crosses zero, and that positive estimates are for research purposes only, and not a recommendation to buy or sell the stock. The manager observes full metrics over 18730 events. The data comes from the investor website of AVAV and a stock data website.
A news scan found the notes from an automated trading fund manager. The notes mention a system called QEAR91000 for trading AVAV, which is a stock ticker. The system uses a measure called EAR, or Earnings Abnormal Return, which is greater than 0. The system also uses a 5-session horizon, which means it looks at what happens over 5 trading days. It has a stop loss, which is a price level at which the system automatically sells a stock to limit losses, set at 1 times the average daily range, or ADR, below the entry price. The system sells half of the stock at 1 ADR and sets a stop loss at the break-even point for the remaining half. The system was trained on data from 2016 to 2020 and tested on data from 2021 to 2025. The system has an initial cash amount of 1000 USD and uses fractional shares, which means it can buy a fraction of a share. The system does not have fixed business costs and has a cash ledger that is reconciled with a maximum error of 0. The system's drawdown, or loss, is calculated using the opening and closing prices of the stock, not the full intraday risk. The system uses a single-name proxy, which is a stock that represents the overall market, in this case, SPY. The system has only 9 test events and the estimates are for research purposes only, not approval. The full metrics observation is 18729. The sources of the data are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes from an automated trading fund manager. The notes mention a system called QEAR91000 for the stock AVAV. The system uses a method called EAR, which stands for Earnings Announcement Return, to decide when to buy or sell. The notes say to buy AVAV on the second day after the company announces its earnings, but only if EAR is greater than 0. The system looks at how AVAV performs compared to the SPY stock, which is a collection of many stocks, over a few days. It uses a five-day plan and sets a stop loss, which is a price at which to automatically sell, at the average high and low prices of the previous 20 days. The system also sets a target price, which is 1.5 times the average daily price range. The notes mention that the system was trained using data from 2016 to 2020 and tested using data from 2021 to 2025, with a separate test in 2026. The system uses a method called vectorbt, which is a way of analyzing and making decisions about stocks, and it starts with 1000 USD in cash. The notes also mention that the system has a fee of 1 basis point, which is a unit of measurement for interest rates and fees, plus a slippage of 15 basis points on each side of the trade. A basis point is one hundredth of a percent. The system uses fractional shares, which means it can buy and sell parts of a share, and it does not have fixed business costs. The cash ledger, which is a record of the system's cash transactions, has been reconciled and the maximum error is 0. The system calculates drawdown, which is the decrease in value of the system's portfolio, using the opening and closing prices of the stocks, not the full range of prices during the day. The notes also mention that this system is not the same as a long-short replication, which is a way of investing that involves buying and selling stocks to balance risk, and that it is based on a single stock, AVAV, as a proxy for the SPY stock. The signal family, which is a group of related signals, has only 9 test events, and the uncertainty of the estimates crosses zero, which means that the system is not always sure about its predictions. The notes mention that the positive estimates are for research purposes only and not approval. The system has 18728 full metrics observations. The sources of the data are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes from an automated trading fund manager. The manager uses a system called QEAR91000 for the stock AVAV, which is an extension with specific settings: fixed 5, EAR greater than 0, and 6 basis points per side. The notes reference frozen notes 1683, 1684, and 1688. The manager looks at the earnings dates that are visible to the issuer, and calculates the return of AVAV compared to SPY over three sessions, through the day after the event, and only enters a trade on the second day after the event if EAR is greater than 0. The manager uses a five-session horizon, with a stop set at the prior 20-session mean high-low, and a target of 1.5 average daily range. The manager also considers daily open-high-low-close stops, ambiguity at the opening, and capped target improvements. The system was trained on data from 2016 to 2020, and tested on data from 2021 to 2025, with a separate extension for 2026, and all entries and exits are within a partition. The fees are 1 basis point plus slippage of 5 basis points on each side of the trade. The manager uses a long-only strategy, with an initial cash amount of 1000 USD, fractional shares, and no fixed business costs. The cash ledger has been reconciled with a maximum error of 0. The drawdown is calculated using open and close marks, not full intraday risk. This system uses AVAV as a proxy for SPY, but it is not a replication of the long-short strategy from the paper, which is adjusted for size and value. The signal family has only 9 test events, and the uncertainty crosses zero, with positive estimates considered research, not approval. The full metrics observation is 18727. The sources of the data are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes from an automated trading fund manager. The notes mention specific rules for buying and selling the stock AVAV. The rules are based on the stock's performance compared to the SPY stock, and on the company's earnings dates, which are the dates when the company announces its profits. The system looks at how well AVAV did compared to SPY over three days, and then decides whether to buy AVAV on the next day, but only if a specific measure called EAR is greater than 0. The system uses a five-day plan and sets a stop-loss, which is a price at which to automatically sell the stock to limit losses, at the average high and low prices of the previous 20 days. It also sets a target price, which is 1.5 times the average daily price range. The system was trained using data from 2016 to 2020, tested using data from 2021 to 2025, and will be extended using data from 2026. The system starts with 1000 USD and can buy fractional shares. There are no fees, and the system accounts for slippage, which is the difference between the expected and actual price of a trade. The system only buys stocks, does not sell short, and does not have fixed business costs. The cash ledger, which is the record of all transactions, has been checked and has a maximum error of 0. The system calculates drawdown, which is the measure of how much the stock price falls from its peak, using the opening and closing prices, not the full range of prices during the day. The system uses AVAV as a proxy for the SPY stock, but this is not the same as the long-short replication strategy described in a research paper. The signal family, which is the group of rules used to decide when to buy and sell, has only 9 test events, and the uncertainty of the estimates crosses zero, meaning that the estimates are not reliable. The system has observed 18726 full metrics. The data comes from https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the note about QEAR91000 AVAV extension with time5, EAR greater than 0, and 16 basis points per side. The notes are based on frozen data from 1683, 1684, and 1688. The issuer-visible earnings dates are used to calculate the three-session return of AVAV minus SPY, which is complete through the day after the event, and a position is entered at the open of the second day after the event only if the EAR is greater than 0. The position is exited after five sessions, with no stops in place. There is a daily check for stop-first ambiguity, which means checking if a stop or a limit order should be executed first, and adverse opening gaps at the open, with target improvement capped. The model was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and the 2026 extension is separate, with entries and exits within each partition. The fees are 1 basis point plus slippage of 15 basis points on each side of the trade. The model uses vectorbt, which is a backtesting library, with a long-only strategy, an initial cash amount of 1000 USD, fractional shares, and no fixed business costs. The cash ledger has been reconciled, with a maximum error of 1.1368683772161603e-13. The drawdown, which is the peak-to-trough decline in value, uses the open and close marks, not the full intraday risk. This selected single-name SPY proxy, which is a benchmark, is not the same as the paper's cross-sectional size and value-adjusted long-short replication. The signal family, which refers to a group of related trading signals, has only 9 test events, and the uncertainty crosses zero, which means the model is not always certain about its predictions. The positive estimates are for research purposes only, not for approval. The full metrics observation is 18725. The data sources are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.