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.
The automated trading fund manager has frozen notes numbered 1683, 1684, and 1688. These notes are about the earnings dates of issuers that can be seen by the public. The manager looks at the return of AVAV stock compared to the SPY stock over three sessions, and completes this comparison through the day after the earnings announcement. The manager will enter a trade on the second day after the earnings announcement, but only if the Earnings Announcement Return, or EAR, is greater than 0. The EAR is a measure of how the stock price changes after an earnings announcement. The manager will exit the trade after five sessions, and there are no stop-loss orders. The manager uses a system called vectorbt, which is a long-only strategy, meaning it only buys stocks and never sells short. The initial cash investment is 1000 USD, and the manager can buy fractional shares. There are no fixed business costs, and the fees are 0 basis points, or 0bp, plus slippage of 0bp on each side of the trade. A basis point is a unit of measurement for interest rates and fees. The manager has tested this strategy from 2016 to 2020, and then from 2021 to 2025, with a separate test in 2026. The manager uses a proxy, or substitute, for the SPY stock, but this is not the same as the long-short replication strategy used in the research paper. The signal family, or group of signals, has only 9 test events, and the uncertainty of the estimates crosses zero. The manager has observed full metrics 18699 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 from a trading fund manager. The manager used a system called QEAR91000 to trade the stock AVAV. The system looked at the company's earnings dates, which are the dates when the company reports its profits. It also looked at how the stock performed compared to the SPY stock, which is a collection of top stocks, over three sessions. The system would buy the stock on the second day after the earnings report, but only if the EAR, or Earnings Announcement Return, was greater than 0. The EAR is a measure of how the stock performs when the company reports its profits. The system would sell the stock after 20 sessions, which are periods of trading. There were no stops, which are automatic sells to limit losses. The system also looked at the daily high, low, open, and close prices, and would stop trading if there were big gaps in the price at the start of trading. The system was tested from 2016 to 2020, and then from 2021 to 2025. The system was also tested in 2026, but separately. The system used a long-only strategy, which means it only bought stocks, and started with $1000 in cash. It also used fractional shares, which means it could buy a fraction of a stock. The system did not have any fixed business costs. The cash ledger, which is a record of the money, was reconciled, and the maximum error was 0. The drawdown, which is a measure of the biggest loss, was calculated using the open and close prices, not the full risk of the day. The system used the SPY stock as a proxy, which is a stand-in for the overall market. The system had 9 test events, and the uncertainty, or risk, crossed zero. The positive estimates are for research purposes only, and not approval. The system observed 18698 full metrics. 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 headline 'QEAR91000 AVAV train: parent20, EAR>0, 6bp/side'. Frozen notes from 1683 to 1684 and 1688 contain information about an automated trading strategy. The strategy looks at the earnings dates of the issuer, which is the company that issued the stock, and calculates the return of AVAV stock minus the return of SPY stock over three sessions, where a session is a trading day. The strategy enters a trade on the open of the second day after the earnings date 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 earnings announcement. The trade is exited after 20 sessions, which is a period of 20 trading days, with no stops, meaning there are no automatic sell orders if the stock price falls. There is a daily stop-first ambiguity, which means the strategy checks if it should stop the trade or not at the beginning of each day, and adverse opening gaps, which are large price movements at the open, are taken into account. The target improvement is capped, meaning it has a maximum limit. The strategy was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and will be extended to 2026 separately. The strategy only buys stocks, never sells short, and starts with 1000 USD in cash, using fractional shares, which are smaller units of stock than a full share. The strategy does not have fixed business costs. The cash ledger, which is a record of all cash transactions, has been reconciled, meaning it has been checked for errors, and the maximum error found was 2.2737367544323206e-13. The drawdown, which is a measure of the strategy's performance, uses the open and close prices of the stock, not the full intraday risk, which is the risk of loss during the trading day. This strategy uses AVAV stock as a proxy for SPY stock, but it is not the same as the long-short replication strategy described in a research paper, which is a strategy that buys some stocks and sells others short. The signal family, which is a group of related trading signals, has only 9 test events, and the uncertainty of the strategy crosses zero, meaning it is not certain if the strategy will work. The positive estimates are for research purposes only and do not mean the strategy is approved. The full metrics observation is 18697. 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 used a system called QEAR91000 to trade the stock AVAV. The system is based on the idea of earnings, which is when a company makes money, and is visible to the company that issued the stock. The manager looked at how well AVAV did compared to the SPY stock, which is a group of many stocks, over three days. If AVAV did better, the system would buy the stock on the next day. The manager would sell the stock after 20 days, no matter what. The system also had rules to stop trading if the stock started to lose money, and to limit how much money could be made. The manager tested the system from 2016 to 2020, and then from 2021 to 2025, and plans to test it again in 2026. The system was set up to only buy stocks, with 1000 USD to start, and could buy fractions of a stock. There were no extra fees, and the manager made sure the accounting was correct. The system looked at how much money was made or lost from the start to the end of each day, not at the highest or lowest points during the day. The manager used the SPY stock as a comparison, but this is not the same as a more complex system that looks at many stocks and tries to pick the best ones. The system only had 9 test events, and the manager is still researching and does not approve or disapprove of the results. The manager got the data from the AVAV company website and another website. The data included 18696 observations.
A news scan found the headline is not available, but a diagnostic report of a momentum screen was found. The report ran a test on three cryptocurrency pairs, BTC/USD, ETH/USD, and SOL/USD, using a momentum strategy with a lookback period of 3 days and 617 days of data. The results showed a return of -10.948474% for BTC, -35.643586% for ETH, and -39.976039% for SOL, with drawdowns of 33.437946%, 62.105551%, and 60.750072% respectively. The test also reported win rates of 32.58427%, 28.888889%, and 26.373626%, with 89, 90, and 91 trades respectively. The test used the same close signals and fills, without considering fees or slippage. The results include the latest bars from days 618, 617, and 618, which may be partial and from the current day. Therefore, the diagnostic test cannot validate any entry, and even with optimistic gross results, the screen loses. A separate analysis using vectorbt, which includes data from 2025 for training and 2026 for holdout, will supersede the current execution and evaluation method. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars, and all figures are from observations recorded without any selection from the command line interface.
A news scan found the headline 'SOL fixed trend momentum daily/hourly test: reject'. This refers to a test of a trading rule for the cryptocurrency SOL. The rule was based on two premises: the closing price being higher than the 20-day Simple Moving Average, and a positive momentum over 3 days. If these conditions were met, the rule would enter a trade at the next open, and exit at the next open if the conditions failed. The test used hourly data with a 480/72 lookback period, and accounted for fees of 0.25% on each side, plus a 0.10% slippage baseline and 0.20% stress. The test was run from 2025, with an untuned holdout period from January 1 to September 9, 2026, and a warm-up period in November 2024. The initial cash was reset to 100 at each period, and the final open was liquidated. The test also used a neighbors parameter of 15/2 and 30/5 for robustness, but not for holdout selection. A complete slow window was required, and missing bars were not filled. The test did not include stop or target prices in the diagnostic, and failed before the 15-minute exit gate, so it cannot be deployed. The daily and hourly rules were evaluated separately, not just as changes in execution frequency. The baseline hourly results were: symbol SOL, time frame 1 hour, slow window 20, lookback 3, period out of sample, slippage 0.001, return -40.24%, drawdown 58.60%, number of trades 91, win percentage 16.48%, average win 4.78, average loss 1.51, mean -0.47, confidence interval -1.24 to 0.67, Kelly criterion 0. The baseline daily results were: symbol SOL, time frame 1 day, slow window 20, lookback 3, period out of sample, slippage 0.001, return 4.67%, drawdown 35.77%, number of trades 21, win percentage 28.57%, average win 11.05, average loss 3.63, mean 0.56, confidence interval -2.65 to 4.82, Kelly criterion 0.01. All 24 cells were saved in two preceding observations, including training and stress. The test also used a bootstrap diagnostic with 10,000 trades to infer serial dependence and selection limit. The test rejected the family of momentum rules, not just this specific rule. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test was run using local study and test rule scripts, and prior failed rules 1639-41 were separate.
A news scan found the headline 'ETH fixed trend momentum daily/hourly test: reject'. This refers to a test of a trading rule for Ethereum, which is a type of digital currency. The rule, called Premises1, is based on two conditions: the closing price is higher than the average price over the last 20 days, and the momentum, or rate of change, over the last 3 days is positive. A momentum is a measure of how fast the price is changing. If both conditions are met, the rule enters a trade at the next open, and exits at the next open if the conditions are no longer met. The test used hourly and daily time frames, with a lookback period of 480 and 72 hours, respectively. A lookback period is the amount of time used to calculate the conditions. The test also included fees of 0.25% on each side of the trade, plus a slippage baseline of 0.10% and a stress test of 0.20%. Slippage refers to the difference between the expected and actual prices of a trade. The test was run from January 1, 2025, to September 9, 2026, with a warm-up period in November 2024. The initial cash was reset to $100 at the start of each period, and the final open position was liquidated. The test used 15 and 2 neighbors for robustness, but not for holdout selection. A holdout selection is a method of evaluating a trading rule by setting aside a portion of the data. The results showed that the rule failed, with a return of -46.58% and a maximum drawdown of 53.99% for the hourly time frame, and a return of -11.42% and a maximum drawdown of 37.74% for the daily time frame. A drawdown is the maximum loss from a peak to a trough. The test also included a diagnostic to evaluate the performance of the rule, but it failed before the 15-minute exit gate, which means it cannot be deployed. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test was run using local study and test rule scripts, and prior failed rules 1639-41 were separate.
return-46.58
drawdown53.99
win rate14.13
sample92
mean-0.63
avg_win4.08
avg_loss1.4
quarter_Kelly0
daily_return-11.42
daily_N26
AVAV Breakout
BacktestsrejectSupply chainAVAV2026-09-10 00:06
A news scan found the headline 'AVAV costed channel breakout: positive small holdout does not repair negative training'. This refers to a study on the stock AVAV. The study used 1002 adjusted bars from September 12, 2022, to September 9, 2026. It applied a long-only strategy, entering when the close was higher than the previous 20-day high and exiting when the close was lower than the previous 10-day low. The study was divided into training and holdout periods, with fees of 5 basis points per side and slippage of 10 and 30 basis points. A 3x2 sweep was performed, but no winning combination was found. The training period from 2024 showed a return of -24.4721% and a drawdown of 41.4508%. The holdout period from 2025 showed a return of 50.033% and a drawdown of 52.0352%. However, the conclusion was to not enter a trade due to the small sample size, unstable training and holdout periods, and lack of validation. The positive holdout result was not enough to justify entering a trade.
return50.03
drawdown52.04
win rate33.33
sample6
train_return-24.47
train_sample10
avg_win_pct60.1
avg_loss_pct11.66
stress_return46.48
parameter_cells6
approved_usd0
AVAV Breakout
BacktestsrejectSupply chainAVAV2026-09-10 00:06
A computer program tested a trading strategy on the stock AVAV, which is a ticker symbol for a company. The test used a breakout strategy, meaning it looked for when the stock price breaks out of a certain range, and it looked back at 20 days of data over 1460 days. The test stopped trading when it lost 3 percent of its value and aimed to make a 4.5 percent profit. The test used data from a website and found that the strategy had a 72.9481 percent return, a 13.3253 percent drawdown, and a 47.2973 percent win rate, with an average trade return of 0.79278 percent. The test also compared the strategy to a benchmark, called buy and hold, which had a 40.94047 percent return. However, the test was just exploratory and did not verify some assumptions about costs, so it does not authorize actual trading. Another test using a different method and costs was done separately and should not be compared directly to this test. The result of the test was attractive, but it needs to be tested further in a real-world setting before it can be used for actual trading, and no parameters from the test are recommended for use.
A news scan found the headline 'BTC fixed trend momentum daily/hourly test: reject'. This refers to a test of a trading rule for Bitcoin. The rule is based on two conditions: the closing price is higher than the average price over the last 20 days, and the momentum, or rate of change, over the last 3 days is positive. If both conditions are met, the rule enters a trade at the next open, and exits at the next open if the conditions are no longer met. The test used hourly data with a lookback period of 480 and 72 hours, and accounted for fees of 0.25% on each side of the trade, as well as slippage of 0.10% and stress of 0.20%. The test was run from January 1, 2026, to September 9, 2026, with a warm-up period in November 2024, and used an initial cash balance of $100 that was reset at the start of each period. The test also used a final open liquidation, meaning that any open trades were closed at the end of the test. The results of the test showed that the rule did not perform well, with a return of -43.15% and a maximum drawdown of 52.33% over 91 trades on an hourly basis, and a return of -6.24% and a maximum drawdown of 27.17% over 24 trades on a daily basis. The test also showed that the rule had a low win percentage, with only 9.89% of trades being winners on an hourly basis, and 29.17% of trades being winners on a daily basis. The test used a bootstrap method to estimate the confidence interval of the results, and found that the rule did not perform well enough to be deployed. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the test was run using local study and test rule scripts.
A news scan found the headline that a required command line interface, or CLI, was run for the stock ticker LOVE. The command was node /app/agent/dist/backtest.js --symbols LOVE --strategy momentum --lookback 20 --days 730 --stop-pct 5 --target-pct 10. This means the computer program looked at the stock LOVE using a momentum strategy, which is a way of making decisions based on the stock's price movement, and considered the last 20 days of price movement over the last 730 days. The program also used a stop loss, which is a rule to sell a stock when it loses a certain percentage of its value, in this case 5 percent, and a target profit, which is a rule to sell a stock when it gains a certain percentage of its value, in this case 10 percent. The result was 501 daily price movements, or bars, and 56 trades, with a return of -17.85047 percent, a drawdown, which is the largest decrease in value, of 44.98827 percent, a win rate of 32.14286 percent, and a mean trade, which is the average profit or loss per trade, of -0.164327 percent. The return of buying and holding the stock was -22.94455 percent. The conclusion is that this momentum strategy does not work for the stock LOVE, and the actual experiment that was selected uses fees, which are costs of buying and selling the stock, and slippage, which is the difference between the expected price of a trade and the actual price. The first output of the command was cut off, so the command was run again and the exact results were saved to a file called love_cli.json to verify the results, which included 56 trades. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LOVE&timeframe=1Day&feed=iex, which is a source of stock price data.
A trading analysis of LOVE was done using a method called vectorbt, which is a way to backtest trading strategies. The analysis used 16 training periods and 10 holdout periods, which are like test periods. The strategy had two main rules: a stop-loss, which is a price level that triggers a sale, set at the average high-low range of the prior 20 days, and a target price, which is twice that range from the 09:30 open. The strategy also had a mandatory exit time of 15:45. The results showed a return of 2.3062% and a drawdown of 12.2869% in one scenario, and a return of 0.2804% and a drawdown of 12.9858% in another scenario. The win rate was 30% in the first scenario and 20% in the second scenario. The analysis also calculated a quarterKelly, which is a measure of the strategy's performance, and found it to be 0.0134 in the first scenario and 0.0050 in the second scenario. However, the confidence intervals, which are like margins of error, crossed zero, meaning that the results are not statistically significant. The data used for the analysis came from a source called sip, and the analysis was done on 15-minute bars from 18186 to 18233. The results should be interpreted with caution, as the quarterKelly estimate does not override the uncertainty of the results, and the operational risk is still present.
A trading analysis of LOVE was done using a method called vectorbt, which is a way to backtest trading strategies. The analysis used 16 training periods and 10 test periods. The strategy used a stop loss, which is a price level at which to automatically sell a stock, set at the average high-low range of the prior 20 days. The target price, which is the price at which to sell for a profit, was set at 1.5 times that range from the 09:30 open. The strategy was tested on 15-minute intervals and had to exit the trade by 15:45. The results showed a return of -0.37% and -2.34% in two different tests, with a win rate of 30% and 20%. The results also showed a drawdown, which is a measure of the biggest decline in value, of 12.29% and 12.99%. The analysis used a fee of 0.10% per trade and a slippage, which is the difference between the expected price and the actual price, of 0.10% and 0.20%. The results were uncertain, with the confidence intervals, which are a measure of the range of possible outcomes, crossing zero. This means that the strategy may not be profitable. The data used for the analysis came from a source called sip and was adjusted for all factors. The analysis was done on a 15-minute timeframe and did not include overnight holding. The quarterKelly, which is a measure of the optimal fraction of wealth to invest, was 0.0047 and 0, but this did not override the uncertainty of the results.
A trading fund manager analyzed the stock LOVE. The analysis had four main points. First, the manager tested a trading strategy on LOVE stock from 2019 to 2023 and then applied it to 2024 to June 2026. This strategy was not tested on new, independent data. Second, the manager used a type of trading simulation called Vectorbt, which buys and sells stocks without selling short, meaning it only buys stocks. The simulation used 15-minute stock prices and included a 0.1% fee and 0.1% slip, which is a small loss when buying or selling a stock. The manager analyzed the LOVE and SPY stocks, but some data was missing. Third, the strategy that only considered time performed well, with an 8.7623% return and a 19.8103% maximum loss. The strategy that was tested on the holdout data, which is data not used in training, performed even better, with a 14.8701% return and an 11.2663% maximum loss. However, when the manager added stops, which are automatic sell orders to limit losses, and targets, which are automatic sell orders to lock in profits, the results were not as good. Fourth, the manager concluded that this strategy should not be used for trading, but it may be worth studying further. The manager used stock price data from a website and information about the company's upcoming earnings report from the company's website. The analysis was based on data from 2019 to June 2026, but the manager did not verify the completeness or accuracy of the data.
A news scan found the headline 'QIR90908 SPY extension IBS: all29 frozen cells'. This refers to a trading strategy update. The update mentions a 2026 through September 8 extension, not a model selection. The main strategy was completed prior to the IBS, which is a measure of volatility, being less than or equal to 0.2. The next session will open long, with a stop at 1 prior 20 session average daily range and a target at 1.5 times the average daily range, or close. There is an adverse barrier first. The strategy has an independent 1000 USD cash sleeve, using a vector backtest for long only trades, with a proportional 1 basis point fee plus 5 basis points lip on each side, and stress 1 plus 15. All 29 cells have thresholds of 0.1, 0.2, and 0.3, with open, managed, and green statuses, and fees of 0, 6, and 16 basis points. There are 2 buy holds in observations 18270, 18271, 18272, 18273, and 18274. The strategy uses a green half plus 1 average daily range, then a breakeven or target on the remainder, with conservative daily ordering. The average exit matches the proportional cost of cash, but not fixed fees, with 15 minute whole lot execution. The synthetic open and exit marks are not full intraday. The trade return denominator entry value excludes the entry fee, and the cash mean is the mean divided by 1.0001 for costed cells. The strategy uses a complete 8 asset fixed universe, with all 2685 dates from 2016 to September 8, with no survivorship bias for all ETFs. The same protocol is used in the holdout from 1642 to 1649, and a positive train or recent result does not qualify alone. The source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SPY,QQQ,IWM,EEM,EFA,EWJ,EWZ,FXI&timeframe=1Day&feed=sip&adjustment=all.
A news scan found the headline 'QIR90908 QQQ extension IBS: all29 frozen cells'. This refers to a trading strategy, where IBS stands for a statistical method to identify trends. The details are as follows: Frozen1431, with a 2026 through September 8 extension, not based on model selection. The main strategy was completed prior to IBS being less than or equal to 0.2, with the next session opening long, and a stop at 1 prior 20 session average daily range, or a target at 1.5 times the average daily range, or close. The first adverse barrier was considered. There was an independent 1000 USD cash sleeve, with a vectorbt long only strategy and a proportional 1 basis point fee plus 5 basis points lip on each side, with stress at 1 plus 15. All 29 cells had thresholds at 0.1, 0.2, and 0.3, with open, managed, and green statuses, and fees at 0, 6, and 16 basis points. There were 2 buy holds in observations 18265, 18266, 18267, 18268, and 18269. The strategy involved a green half plus 1 average daily range, then a break even or target on the remainder, with conservative daily ordering. The average exit matched the proportional cost of cash, but not fixed fees, with 15 minute whole lot execution. The synthetic open and exit marks were not full intraday. The trade return denominator entry value excluded the entry fee, and the cash mean was calculated as the mean divided by 1.0001 for costed cells. The strategy was based on a complete 8 asset fixed universe, with all 2685 dates from 2016 to September 8, and no survivorship bias for all ETF claims. The same protocol was used in the holdout period from 1642 to 1649, and a positive train or recent result does not qualify alone. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SPY,QQQ,IWM,EEM,EFA,EWJ,EWZ,FXI&timeframe=1Day&feed=sip&adjustment=all.