A news scan found the note about QEAR91000 AVAV extension with specific conditions. The conditions are time5, EAR greater than 0, and 6 basis points per side. The notes reference frozen notes 1683, 1684, and 1688. The strategy is based on issuer-visible earnings dates and a three-session return of AVAV minus SPY, complete through t+1. The decision to enter is made at t+2 open only if EAR is greater than 0. The exit is based on a five-session close, with no stops. There is a daily OHLC stop-first ambiguity and adverse opening gaps at open, with target improvement capped. The training data is from 2016 to 2020, and the test data is from 2021 to 2025, with 2026 being a separate extension. Entries and exits are within a partition. The fees are 1 basis point plus slippage of 5 basis points on each side. The strategy uses vectorbt, which is a long-only strategy, 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 2.2737367544323206e-13. The drawdown uses open and close marks, not full intraday risk. This strategy is a single-name SPY proxy and not a cross-sectional size and value-adjusted long-short replication. The signal family has only 9 test events, with uncertainty crossing zero, and positive estimates are for research purposes only, not approval. The full metrics observation is 18724. 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 for QEAR91000 AVAV extension. The notes mention frozen notes 1683, 1684, and 1688, which refer to a set of rules for automated trading. The rules are based on the issuer-visible earnings dates, which is when a company publicly releases its financial information, and the three-session return of AVAV stock minus the return of the SPY stock, which is a type of investment fund. The rules state that the system should enter a trade at the open of the next trading day, t+2, only if the EAR, or Earnings Announcement Return, is greater than 0. The EAR is a measure of how the stock price reacts to the company's earnings announcement. The system will exit the trade after five sessions, based on the closing price, with no stops, which are orders to sell a stock when it falls to a certain price. The system also considers daily high, low, open, and close prices, known as OHLC, to handle ambiguity and adverse opening gaps. The system was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and extended to 2026. The system uses a long-only strategy, meaning it only buys stocks, with an initial cash amount of 1000 USD, and allows for fractional shares, which are smaller units of stock. The system has no fixed business costs and reconciles its cash ledger, with a maximum error of 0. The system measures drawdown, which is the decrease in value of the investment, using open and close marks, not full intraday risk. The system uses the SPY stock as a proxy, which is a stand-in for the overall market, but this is not the same as the cross-sectional size and value-adjusted long-short replication, which is a more complex investment strategy. The signal family, which refers to the set of rules used to generate buy and sell signals, has only 9 test events, and the uncertainty of the estimates crosses zero, meaning that the system is not certain about its predictions. The system has observed 18723 full metrics, which are measurements of the system's performance. 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 for QEAR91000 AVAV extension. The notes mention frozen notes 1683, 1684, and 1688. The strategy is based on issuer-visible earnings dates and a three-session return of AVAV minus SPY, complete through the day after the event, and entering the trade on the open of the second day after the event only if the EAR is greater than 0. The trade is exited after 20 sessions, with no stops. There is a daily stop based on the highest and lowest prices, and the target improvement is capped. The strategy was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and extended in 2026. The fees are 1 basis point plus 15 basis points for slippage on each side of the trade. The strategy uses a long-only approach with an initial cash amount of 1000 USD, allows for fractional shares, and has no fixed business costs. The cash ledger has been reconciled with a maximum error of 0. The drawdown is calculated using the opening and closing prices, not the full intraday risk. The strategy 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 considered research, not approval. The full metrics observation is 18722. The sources of the data are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
The notes from the automated trading fund manager are as follows: the system uses frozen notes 1683, 1684, and 1688. It looks at the earnings dates that the company that issued the stock can see. The system calculates the return of AVAV stock minus the return of the SPY stock over three sessions, and completes this calculation through the day after the earnings announcement. It only enters a trade at the open of the second day after the earnings announcement if the EAR, or Earnings Announcement Return, is greater than 0. The system exits the trade after 20 sessions, and does not use stop-loss orders. It also considers the daily high, low, open, and close prices to avoid ambiguity and limit losses. The system was trained on data from 2016 to 2020, and tested on data from 2021 to 2025. The 2026 extension is separate, and all trades are within a specific partition. The fees are 1 basis point plus 5 basis points of slippage on each side of the trade. The system uses a long-only strategy, starts with 1000 USD in cash, allows fractional shares, and does not have fixed business costs. The cash ledger has been reconciled, with a maximum error of 2.2737367544323206e-13. The drawdown calculation uses the open and close prices, not the full intraday risk. This system uses AVAV as a proxy for the SPY stock, but it is not the same as the cross-sectional size and value-adjusted long-short replication in the research paper. The signal family has only 9 test events, and the uncertainty crosses zero. Positive estimates are for research purposes only, and not approval. The system has observed 18721 full metrics. 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 for QEAR91000 AVAV extension. The notes mention frozen notes 1683, 1684, and 1688. The issuer's earnings dates are visible, and the return of AVAV minus SPY is calculated over three sessions, complete through the day after tomorrow, or t+1. A position is entered at the open of t+2 only if the EAR, or Earnings Announcement Return, is greater than 0. The position is exited after 20 sessions, with no stops. There is a daily stop based on the high, low, open, and close prices, with ambiguity resolved by stopping first, and adverse opening gaps at the open, with target improvement capped. The system was trained on data from 2016 to 2020, and tested on data from 2021 to 2025, with a separate extension in 2026. Entries and exits are within a partition. Fees are 0 basis points, or 0bp, plus slippage of 0bp on each side of the trade. The system uses vectorbt, which is a long-only strategy, with an initial cash amount of 1000 USD, and allows for fractional shares, with no fixed business costs. The cash ledger has been reconciled, with a maximum error of 0. Drawdown is calculated using the open and close prices, not the full intraday risk. This 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, with uncertainty crossing zero, and positive estimates are for research purposes only, not approval. The full metrics observation is 18720. 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 uses a system called QEAR91000 to decide when to buy or sell the stock AVAV. The system checks if the stock's earnings are visible to the company that issued it, and if the stock's return is better than the return of the SPY stock, which is a group of stocks used for comparison. The manager only buys the stock on the second day after the earnings are announced, if the stock's return is better than the SPY's return. The manager sells half of the stock when its price increases by 1 times the average daily range, and sells the rest when the price reaches the break-even point. The manager uses a computer program called Vectorbt to make these decisions, with an initial investment of 1000 USD and the ability to buy fractional shares. The manager also pays a fee of 1 basis point, which is a unit of measurement for interest rates and investment returns, and 15 basis points for each buy and sell transaction. A basis point is equal to 0.01 percentage points. The manager tested this system using data from 2016 to 2020, and then used it in real-time from 2021 to 2025, with a separate test in 2026. The manager got the stock's financial information from the website https://investor.avinc.com/financial-information/quarterly-results and the stock's price data from 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 AVAV with a green5 signal, EAR greater than 0, and 6 basis points per side. The notes reference frozen notes 1683, 1684, and 1688, which are about issuer-visible earnings dates. The manager looks at the three-session return of AVAV minus SPY, complete through t+1, and enters at the t+2 open only if EAR is greater than 0. The manager uses a five-session horizon, with a stop at the prior 20-session mean high-low, and a target at 1.5 average daily range. The manager sells half at 1 average daily range and sets a stop at breakeven from the next session. The manager also considers daily open-high-low-close stops and ambiguity, with adverse opening gaps at the open, and target improvement capped. The manager trained the model from 2016 to 2020, tested it from 2021 to 2025, and has a separate extension for 2026, with entries and exits within a partition. The fees are 1 basis point plus slippage of 5 basis points on each side. The manager uses a long-only strategy with an initial cash of 1000 USD, fractional shares, and no fixed business costs. The cash ledger is reconciled with a maximum error of 0. The drawdown uses open and close marks, not full intraday risk. This is a single-name SPY proxy, not a cross-sectional size and value-adjusted long-short replication. The signal family has only 9 test events, with uncertainty crossing zero, and positive estimates are for research purposes only, not approval. The full metrics observation is 18718. The sources 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 the stock AVAV. The system looks at the earnings dates of the company that issues AVAV, which is a time when the company tells the public how much money it made. The manager also looks at how the price of AVAV changes over three days compared to the price of SPY, which is a group of stocks that represents the whole market. If the price of AVAV goes up more than SPY, the manager will buy AVAV on the next day. The manager will sell AVAV if its price goes down to a certain level, which is 1.5 times the average daily price range, or if it goes up to a certain level, which is 1 times the average daily price range. The manager will also sell half of the AVAV stocks if they go up to a certain level. The manager uses a computer program called Vectorbt to make these decisions, and starts with 1000 USD in cash. The manager does not pay any fees to buy or sell stocks, but may lose some money due to the difference between the price they want to pay and the actual price they pay. The manager looks at the prices of AVAV and SPY from 2016 to 2020 to test the system, and from 2021 to 2025 to see how it works in real life. The manager gets the prices of AVAV from the company's website and from a website called runway.adityaperswal.workers.dev. The manager notes that this system is not the same as a system that buys and sells many stocks at the same time, and that the results are not guaranteed.
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 looks at the earnings dates of the company that issues AVAV. It calculates the return on AVAV compared to the return on SPY over three sessions, and only enters a trade at the open of the next session if the EAR, or earnings abnormal return, is greater than 0. The system has a five-session horizon and uses a stop loss set at the prior 20-session mean high-low, with a target of 1.5 times the average daily range. The system also considers daily open, high, low, and close prices to avoid ambiguity and limit losses. The system was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and will be extended to 2026 separately. The system uses a vectorbt long-only strategy with an initial cash amount of 1000 USD, allows fractional shares, and does not account for fixed business costs. The cash ledger has been reconciled with a maximum error of 6.821210263296962e-13. The system calculates drawdown using open and close prices, not full intraday risk. The system uses AVAV as a proxy for SPY, but this is not the same as the long-short replication used in the paper. The signal family has only 9 test events and the uncertainty crosses zero, meaning that positive estimates are for research purposes only and not approval. The system has 18716 full metrics observations. The data sources 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 a trading fund manager. The manager is discussing a strategy for trading the stock AVAV. The strategy is based on the company's earnings dates, which are the dates when the company releases its financial reports. The manager looks at how the stock performs in the three days after the earnings date compared to the performance of the SPY stock index. If the stock performs better, the manager will buy the stock on the next trading day. The manager will sell the stock if it falls below a certain price, which is the average price of the stock over the past 20 days, or if it reaches a target price that is 1.5 times the average daily price range. The manager tested this strategy using data from 2016 to 2020 and then applied it to data from 2021 to 2025. The manager also tested an extension of the strategy in 2026. The manager pays a fee of 1 basis point, which is 0.01%, plus a slippage fee of 5 basis points, which is 0.05%, on each buy and sell transaction. The manager uses a computer program called Vectorbt to make trades, starting with an initial cash amount of 1000 USD and allowing for fractional shares, which means buying a portion of a share. The manager also reconciles the cash ledger, which means checking the accuracy of the cash transactions, and found a maximum error of 6.821210263296962e-13. The manager calculates the drawdown, which is the maximum loss of value, using the opening and closing prices of the stock, not the full range of prices during the day. The manager notes that this strategy is not the same as a strategy that uses a combination of long and short positions, which means buying and selling stocks to profit from their relative performance. The manager also notes that the signal, or the trigger to buy or sell, is based on only 9 test events and that the estimates are for research purposes only, not as a recommendation to buy or sell. The manager used data from the AVAV company website and a data service called Runway to test the strategy. The manager observed 18715 data points.
A news scan found the notes from 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 looks at how AVAV does over three days after the earnings date and buys the stock on the next day if it does better than SPY. The manager sells the stock after five days or when it reaches a certain price, which is 1.5 times the average daily price movement. The manager also stops buying if the stock's price moves too much in one day. The manager tested these rules using data from 2016 to 2020 and is now using them in 2021 to 2025, with a separate test in 2026. The manager does not charge fees and uses a computer program called Vectorbt to make trades with $1000 in initial cash, allowing for fractional shares. The manager's accounting is accurate, with a very small error of 2.2737367544323206e-13. The manager measures risk using the stock's opening and closing prices, not its prices throughout the day. The manager uses AVAV as a substitute for the SPY stock, but this is not the same as the method used in a research paper. The manager has only tested this method nine times and is still researching, not approving, the results. The manager got the 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 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 earnings date, but only if the EAR value is greater than 0, and with a time limit of 5 periods, at a rate of 16 basis points per side. The manager will sell the stock after 5 days, or if certain conditions are met. The system was trained using data from 2016 to 2020, and tested using data from 2021 to 2025. The manager also plans to use data from 2026. The manager pays fees of 1 basis point plus 15 basis points for each buy and sell, and uses a system called vectorbt to make trades. The manager started with $1000 in cash and can buy fractional shares. The manager's cash ledger is accurate to a very small error of 2.2737367544323206e-13. The manager measures risk using the difference between the stock's open and close prices, not the full range of prices during the day. This system is not the same as a more complex system that looks at many stocks and adjusts for size and value. The system has only 9 test events and the results are uncertain. The manager got 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 of a trading fund manager. The manager is discussing a strategy for trading the stock AVAV. The strategy is based on the company's earnings dates, which are the dates when the company releases its financial reports. The manager looks at how the stock performs compared to the SPY stock, which is a proxy for the overall market, over a few days after the earnings date. If the stock performs better than the SPY, the manager will buy the stock on the next open. The manager will sell the stock after five days, or if the stock price moves against them, but only at the close of the day. The manager also considers the daily high, low, open, and close prices to decide when to sell. The strategy was tested using data from 2016 to 2020, and then from 2021 to 2025, with a separate test in 2026. The manager uses a system called vectorbt, which allows them to buy and sell fractions of a stock, and they started with 1000 USD in cash. The manager also considers fees of 1 basis point, which is a unit of measurement for interest rates and fees, and slippage of 5 basis points on each buy and sell. A basis point is one hundredth of a percent. The manager found that the cash ledger, which is a record of all the transactions, was accurate to a very small error of 2.2737367544323206e-13. The manager also looks at the drawdown, which is the maximum loss of value, but only considers the opening and closing prices, not the full range of prices during the day. The manager notes that this strategy is not the same as a more complex strategy that considers the size and value of many stocks. The manager also notes that there were only 9 test events, which means that the results may not be reliable, and that the positive estimates are just for research and not a recommendation to buy or sell the stock. The manager used data from the AVAV website and another website to make these calculations.
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 looks at the earnings dates of the company that issues AVAV, which is a time when the company reports its profits. The manager checks the return on AVAV compared to the return on SPY, a type of stock market index, over three sessions, or trading days. If the return on AVAV is higher, the manager will buy AVAV at the open of the next trading day, which is called t+2. The manager will sell AVAV after five sessions, or trading days, are closed, without using stops, which are orders to sell a stock when it falls to a certain price. The manager uses a type of computer program called vectorbt to make these decisions, with an initial amount of $1000 and the ability to buy fractional shares, which are smaller parts of a whole share. The manager also considers the uncertainty of the system, which has only 9 test events, and notes that positive estimates are for research purposes only, not approval. The manager uses data from the AVAV company website and another website to make these decisions. 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 manager reports that the cash ledger, which is a record of all the transactions, has been reconciled and has a maximum error of 0. The manager also notes that the drawdown, which is the decrease in value of the investment, is calculated using the opening and closing prices of the stock, not the full range of prices during the day.
A news scan found the notes of a trading fund manager. The notes are about trading the stock AVAV. The manager uses a system called EAR, which means Earnings Announcement Return, to decide when to buy or sell. The system checks if the stock's return is greater than 0, or EAR>0, after the company announces its earnings. The manager looks at the stock's price over three sessions after the earnings announcement and compares it to the price of the SPY stock index. If the stock's return is greater than 0, the manager buys the stock at the open of the next session, which is called t+2. The manager sells the stock after 20 sessions, or closes the trade, without using stop-loss orders. The manager also uses a system to limit losses, which is called a daily OHLC stop-first ambiguity. This system checks for adverse opening gaps at the open of the session and limits the target price improvement. The manager tested the system using data from 2016 to 2020 and then from 2021 to 2025. The manager also extended the test to 2026. The manager uses a system called Vectorbt, which is a long-only system, meaning it only buys stocks and does not sell short. The manager starts with 1000 USD in cash and uses fractional shares, which means buying a fraction of a share. The manager does not have fixed business costs. The cash ledger is reconciled, which means it is checked for errors, and the maximum error is very small, 4.547473508864641e-13. The manager calculates the drawdown, which is the loss of value, using the open and close prices, not the full intraday risk. The manager uses AVAV as a proxy for the SPY stock index, but this is not the same as the paper's cross-sectional size/value-adjusted long-short replication. The signal family, which is the group of signals used to make trading decisions, has only 9 test events, and the uncertainty crosses zero, which means the manager is not sure if the signals are correct. The manager has observed full metrics 18710 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 the stock AVAV. 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 on the second day after the company reports its earnings, but only if the EAR value is greater than 0. The manager will sell AVAV after 20 days, or if the stock price moves against them. The manager uses a computer program called Vectorbt to test the system, starting with $1000. The program buys and sells fractions of shares and does not account for business costs. The manager checks the cash ledger to make sure it is accurate, with a maximum error of 2.2737367544323206e-13. The manager measures the risk of losing money using the opening and closing prices of the stock. The system is tested using historical data from 2016 to 2020 and 2021 to 2025, with a separate test in 2026. The manager gets data from the AVAV investor website and a stock data website. The manager notes that the system has only 9 test events and the results are not certain. The manager also notes that the system is not a complete replica of a long-short investment strategy. The manager observes 18709 metrics. The fees for buying and selling are 1 basis point plus 5 basis points for each side, which means 6 basis points in total for each trade. A basis point is a unit of measurement for interest rates and fees, where 1 basis point is equal to 0.01%. The manager uses a stop-first approach to handle ambiguity in daily high and low prices, and to deal with adverse opening gaps at the start of trading.
A news scan found the notes of a trading fund manager. The manager has rules for buying and selling AVAV stock. They look at the earnings dates that the company makes public. They calculate the return on AVAV stock compared to the SPY stock index over three days, and then buy AVAV stock on the next day if the return is greater than 0. They sell AVAV stock after 20 days, no matter what. The manager uses a system called vectorbt, which means a way of analyzing data to make investment decisions, to make these trades. They started with 1000 USD and can buy fractions of shares. They do not pay any fees, which are costs of buying and selling, and they do not consider the costs of actually making the trades. The manager tested this system using data from 2016 to 2020, and then used it in real time from 2021 to 2025. They got their data from the AVAV company website and another website. The manager is careful to note that this system is not the same as a more complex system that would buy and sell many stocks at the same time, and that the results are not guaranteed.
A news scan found the headline 'QEAR91000 AVAV train: green5, EAR>0, 16bp/side'. This is a note about an automated trading system. The system looks at the earnings dates of companies, including AVAV. It checks the return of AVAV compared to the SPY index over three sessions. If the EAR, or Earnings Announcement Return, is greater than 0, the system enters a trade at the open of the next session. The system uses a five-session horizon, which means it looks at what happens over five sessions. It sets a stop loss at the prior 20-session mean high-low, which is a measure of price volatility, and a target at 1.5 times the average daily range, or ADR. The ADR is the average difference between the high and low prices of a stock over a day. The system sells half of the position at 1 times the ADR and sets a stop loss at breakeven for the remaining position. The system was trained on data from 2016 to 2020 and tested on data from 2021 to 2025. There is a separate test for 2026. The system uses a long-only strategy, which means it only buys stocks, and starts with 1000 USD in cash. It uses fractional shares, which means it can buy a fraction of a share, and does not have fixed business costs. The system reconciles its cash ledger, which means it checks to make sure its cash balance is correct, and has a maximum error of 6.821210263296962e-13. The system calculates drawdown, which is the decrease in value of a portfolio, using the open and close prices of the stocks, not the full intraday risk. The system uses AVAV as a proxy for the SPY index, but this is not the same as the long-short replication used in the paper, which is a strategy that involves buying and selling stocks based on their size and value. The signal family, which is a group of related trading signals, has only 9 test events, and the uncertainty of the estimates crosses zero, which means that the estimates are not certain. The system has 18707 full metrics observations. 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 headline about QEAR91000 AVAV train with the details green5, EAR greater than 0, and 6 basis points per side. The notes are frozen from 1683 to 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 an entry is made at the open of the second day after the event only if the EAR is greater than 0. The strategy has a five-session horizon, with a stop loss set at the prior 20-session mean high-low, and a target of 1.5 average daily range, with half of the position sold at 1 average daily range and the remaining position stopped at breakeven from the next session. The daily open-high-low-close stop-first ambiguity is addressed, and adverse opening gaps at the open are capped. The train data is from 2016 to 2020, with a chronological test from 2021 to 2025, and a 2026 extension separate, with entries and exits within the partition. The fees are 1 basis point plus slippage of 5 basis points on each side. The strategy uses vectorbt, which is a long-only backtesting framework, with an initial cash of 1000 USD, fractional shares, and no fixed business costs. The cash ledger is reconciled, with a maximum error of 2.2737367544323206e-13. The drawdown uses open and close marks, not full intraday risk. This selected single-name SPY proxy is not the paper's cross-sectional size and value-adjusted long-short replication. The signal family has only 9 test events, with uncertainty crossing zero, and positive estimates are research, not approval. The full metrics observation is 18706. 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 headline 'QEAR91000 AVAV train: green5, EAR>0, 0bp/side'. The notes are about an automated trading fund manager's strategy for the stock AVAV. The strategy uses something called earnings dates, which are dates when a company's earnings are made public. It also uses a measure called EAR, which stands for Earnings Anticipation Ratio, a way to gauge how much a stock's price might move before its earnings are announced. The strategy is to enter a trade on the second day after the earnings announcement, but only if EAR is greater than 0. The strategy has a five-session time frame, which means it looks at what happens over five trading days. It uses a stop loss, which is a way to limit losses, set at the average high and low prices of the previous 20 sessions, and a target price, which is 1.5 times the average daily range, or ADR. The ADR is a measure of how much a stock's price moves in a day. The strategy also sells half of the position when it reaches a certain price, and sets a stop loss at the break-even point for the rest. The strategy was tested on data from 2016 to 2020, and then on data from 2021 to 2025, with a separate test in 2026. The strategy uses a platform called Vectorbt, which allows for long-only trades, meaning it only buys stocks, not sells them short. It starts with 1000 USD in cash, and uses fractional shares, which means it can buy a fraction of a share. The strategy does not have any fixed business costs. The cash ledger, which is a record of all the transactions, has been reconciled, and the maximum error is 0. The drawdown, which is a measure of the biggest loss, is calculated using the opening and closing prices, not the full range of prices during the day. The strategy uses AVAV as a proxy for the SPY index, but this is not the same as the long-short replication strategy used in the paper, which is adjusted for size and value. The signal family, which is a group of related signals, has only 9 test events, and the uncertainty of the estimates crosses zero, meaning the estimates are not certain. The full metrics observation is 18705. 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 a trading fund manager. The manager used a system called QEAR91000 to trade the stock AVAV. The system had rules, such as only buying if a certain measure called EAR was greater than 0. The system looked at how AVAV performed compared to the SPY stock, and only entered a trade on the second day after the company announced its earnings. The system used a five-day time frame and had a stop loss, which is a rule to sell a stock when it loses a certain amount of value, set at the average high and low prices of the previous 20 days. The system also had a target price, which was 1.5 times the average daily range. The manager tested the system using data from 2016 to 2020, and then tested it again from 2021 to 2025. The manager also tested the system in 2026 separately. The manager paid fees of 1 basis point, which is a unit of measurement for interest rates and investment returns, and also paid for slippage, which is 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 used a program called Vectorbt to make trades, starting with 1000 US dollars in cash, and allowing for fractional shares, which are smaller pieces of a stock. The manager checked the cash ledger, which is a record of all the transactions, and found a very small error of 1.3642420526593924e-12. The manager also looked at the drawdown, which is the amount of money lost during a certain period, and used the opening and closing prices to calculate it, rather than the full range of prices during the day. The manager noted that this system is not the same as a system that uses many stocks and adjusts for size and value. The signal, or the trigger to make a trade, only happened 9 times, and the manager noted that the estimates are for research purposes only, and not a recommendation to buy or sell. The manager got the data from the AVAV investor website and a website called runway.adityaperswal.workers.dev.
A news scan found the notes from a trading fund manager. The manager used a system called QEAR91000 to trade the stock AVAV. The system had rules, such as only entering a trade at the open on the second day after the company's earnings report, but only if the earnings report was good, or what the manager called EAR greater than 0. The manager also used a stop loss, which is a rule to sell a stock when it falls to a certain price, in this case 1 times the average daily range below the previous 20-session mean high-low range. The manager tested the system from 2016 to 2020 and then from 2021 to 2025, with a separate test in 2026. The manager paid fees of 1 basis point, which is a unit of measurement for interest rates and fees, and also paid for what is called slippage, which is the difference between the expected price of a trade and the actual price, at a rate of 5 basis points on each side of the trade. The manager used a program called Vectorbt to make trades, starting with 1000 US dollars in cash, and allowing for fractional shares, which are shares of stock that are less than a whole share. The manager kept track of the cash and made sure it was accurate, with a maximum error of 6.821210263296962e-13. The manager also tracked the drawdown, which is the amount of money lost during a trading period, using the opening and closing prices of the stock, not the full range of prices during the day. The manager used the stock AVAV as a proxy for the SPY index, which is a collection of stocks that represents the overall stock market. The manager noted 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 is a group of related trading signals, had only 9 test events, and that the uncertainty of the system crossed zero, and that positive estimates are for research purposes only, not approval. The manager observed 18703 full metrics and got 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 a trading fund manager. The manager used a system called QEAR91000 to trade the stock AVAV. The system had rules, such as only entering a trade at the open on the second day after the company's earnings report, but only if the earnings were positive. The system looked at the stock's return compared to the SPY index over three days, and then made a decision on the fourth day. The system used a five-day horizon, and had a stop-loss set at the average high and low prices of the previous 20 sessions. The target price was set at 1.5 times the average daily range. The system was trained on data from 2016 to 2020, and then tested on data from 2021 to 2025. There was also a separate test in 2026. The system had no fees, and used a long-only strategy with an initial cash balance of $1000. The system used fractional shares and had no fixed business costs. The cash ledger was reconciled, and the maximum error was zero. The drawdown was calculated using the open and close prices, not the full intraday risk. The system used a proxy for the SPY index, but this was not the same as the long-short replication used in the research paper. The signal family had only 9 test events, and the uncertainty crossed zero. The system had 18,702 full metrics observations. The data came 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 the QEAR91000 AVAV train with specific conditions. The conditions include time5, EAR greater than 0, and 16 basis points per side. The notes mention frozen notes 1683, 1684, and 1688, which refer to issuer-visible earnings dates. The strategy involves calculating the three-session return of AVAV minus SPY, and entering a trade at the open of the next session, t+2, only if EAR is greater than 0. The trade exits after five sessions, with no stops. There is a daily stop based on the order of high, low, open, and close prices, and adverse opening gaps at the open are considered. The target improvement is capped. The train data is from 2016 to 2020, and the test data is from 2021 to 2025, with a separate extension in 2026. The fees are 1 basis point plus 15 basis points of slippage on each side of the trade. The strategy uses vectorbt, which is a long-only backtesting framework, 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 6.821210263296962e-13. The drawdown calculation uses the open and close prices, not the full intraday risk. This strategy 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 considered research, not approval. The full metrics observation is 18701. 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 to trade the stock AVAV. The system looks at earnings dates that the company that issued the stock makes public. It calculates the return on AVAV compared to the SPY stock, over three sessions, and only enters a trade on the next session if the EAR, or earnings abnormality ratio, is greater than 0. The system exits the trade after five sessions, without using stop-loss orders. The system was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and will be extended to 2026 separately. The system uses a 1 basis point fee, plus 5 basis points for slippage on each side of the trade. A basis point is one hundredth of a percent. The system uses a long-only strategy, starting with 1000 USD in cash, and allows for fractional shares. The cash ledger has been reconciled, with a maximum error of 1.3642420526593924e-12. The system calculates drawdown using open and close prices, not full intraday risk. The system uses a single-name proxy for the SPY stock, which is not the same as the cross-sectional size and value-adjusted long-short replication used in the research paper. The signal family has only 9 test events, and uncertainty crosses zero, meaning the estimates are not certain. The system has observed 18700 full metrics. The data comes from https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.