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A news scan found the notes of a trading fund manager. The manager used a system with daily and hourly data to decide when to enter and exit trades. The system was designed to buy after a pullback, which is a drop in price, within three days of a new 20-day high. The pullback had to be half of the average true range, a measure of price volatility, over the past 20 days. The manager also set a stop-loss, which is a price level at which to automatically sell, at two times the average true range of the previous 20 days. If the trade was not successful within 20 days, it was closed. The manager paid a 0.25% fee when buying and selling, and assumed an additional 0.1% to 0.3% slippage, which is the difference between the expected and actual price of a trade. From 2024 to 2025, the system had a return of -6.34%, a maximum drawdown of 37.68%, and a win rate of 42.86%. It closed seven trades and had no open trades. From January 1 to September 11, 2026, the system had a return of -3.78%, a maximum drawdown of 20.46%, and a win rate of 50%. It closed two trades and had no open trades. The manager noted that the system's performance may not be representative of future results, and that the sample size was small. The manager also noted that missing observations were skipped, and that the system did not substitute for an older model. The data used was from August 2024, and the manager reconciled the cash balance with the credited fees. The manager concluded that the system was for research purposes only, and not for automatic trading. The data sources were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the notes of a trading fund manager. The manager used a strategy called OHLC, which stands for Open, High, Low, Close, a way to show daily and hourly stock prices. The manager also used something called ATR, which means Average True Range, a measure of how much a stock price moves. The strategy was to buy when the stock price pulled back by half of its average movement within three days of reaching a new high. The manager would sell when the stock price reached a certain level or after 20 days. The manager paid a fee of 0.25% to buy and sell. From 2024 to 2025, this strategy made a return of 13.23%, but lost 19.79% at its worst. The strategy won 50% of the time and closed four trades. From January 1 to September 11, 2026, the strategy made a return of -11.01%, lost 15.71% at its worst, and won 33.33% of the time. The manager notes that this strategy is not a substitute for an older strategy used for Ethereum. The manager also notes that the results are based on a small sample and do not guarantee future success. The manager used data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the notes of an automated trading fund manager. The manager used a strategy called C91204 ETH 1Hour half-ATR pullback OHLC exits. This strategy is based on a daily and hourly proxy with specific entry and exit rules. The rules include a half Average True Range, or ATR, pullback within 3 days of a breakout from a 20-day high. The ATR is a measure of price volatility. The strategy also includes a stop-loss order set at 2 times the prior completed day's ATR, with a 20-day timeout. The manager accounted for a 25 basis point buy fee credited in coin and a 25 basis point sell fee in USD, with a 10 basis point slippage baseline and 30 basis point stress on each side. The results of the strategy from 2024 to 2025 showed a return of 59.1821%, a drawdown of 34.8121%, and a win rate of 53.84615384615385%, with 13 closed trades and 1 open trade. From January 1 to September 11, 2026, the return was 19.2067%, the drawdown was 13.7909%, and the win rate was 100%, with 3 closed trades and 1 open trade, and a stress test result of 17.5499%. The manager noted that the strategy's performance may not be representative of future results and that the sample size is small. The manager also noted that missing observations were skipped and not treated as zero returns. The data used for the strategy was sourced from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

These are notes about an automated trading system for Ethereum, also known as ETH. The system uses a daily and hourly proxy with a specific entry and exit strategy. It looks for a half Average True Range, or ATR, pullback within 3 days of a breakout from a 20-day high. The ATR is a measure of how much a price is moving. The system stops trading if it loses a certain amount, specifically 2 times the ATR of the last 20 days, and it has a 20-day time limit. The system also accounts for fees, including a 25 basis point buy fee credited in ETH and a 25 basis point sell fee in USD. A basis point is a unit of measurement for interest rates and fees. The system was trained on data from 2024 to 2025 and had a return of -23.6886%, a drawdown of 34.2138%, and a win rate of 37.5%. It closed 8 trades and had 1 open trade. From January 1 to September 11, 2026, the system had a return of -0.2561%, a drawdown of 19.8702%, and a win rate of 66.6667%. The system is not meant to be used for automatic trading and is for research purposes only. The data used to train the system came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

The notes are about a trading strategy for Bitcoin, called C91204. It uses a daily and hourly proxy with a specific entry and exit method. The strategy buys when the price pulls back by half of the average true range within three days of a new 20-day high. It stops the trade at two times the average true range of the previous 20 days, with a 20-day time limit. The strategy also considers fees, with a 0.25% buy fee credited in Bitcoin and a 0.25% sell fee in USD. There is a 0.10% baseline slip and a 0.30% stress slip on each side. The strategy was trained from 2024 to 2025 and had a return of -17.09%, a drawdown of 31.95%, and a win rate of 41.18%. It closed 17 trades and had no open trades. From January 1 to September 11, 2026, the strategy had a return of 1.30%, a drawdown of 9.54%, and a win rate of 66.67%. It closed 3 trades and had 1 open trade, with a stress percentage of 0.11%. The strategy is compared to a control group with the same costs and exits, which had a return of 5.77% in the holdout period. The strategy is based on two premises: that different time frames and policies cannot be substituted, and that small samples and known hypothesis discovery do not establish a prospective edge. A third premise states that missing observations are skipped and entries are cancelled, not treated as zero returns. The strategy uses a vectorbt library to reconcile cash and credited fees, and marks open positions without invented closes. The intrabar target and stop fills are optimistic about continuous availability, despite conservative ambiguous bar ordering. The conclusion is that this is a research comparator, not an automatic entry strategy, and all 192 cells are retained in observations. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the notes from a trading fund manager. The manager used a system with daily and hourly data to decide when to buy and sell. The system used something called OHLC, which stands for Open, High, Low, Close, to determine when to enter and exit trades. It also used a half-ATR pullback, where ATR means Average True Range, a measure of how much prices move. The system would buy when the price pulled back by half of the average true range within three days of a breakout, which is when the price moves above a previous high. The system would stop buying if the price moved below the average true range of the previous 20 days, and it would stop trading after 20 days. The manager paid a fee of 25 basis points, or 0.25%, when buying and selling, and allowed for a 10 basis point slip, or 0.10%, on each side. The system was trained on data from 2024 to 2025 and had a return of -17.0123%, a drawdown of 26.2433%, and a win rate of 20%. It closed five trades and had no open trades. From January 1 to September 11, 2026, the system had a return of 6.8464%, a drawdown of 7.7723%, and a win rate of 66.66666666666666%. It closed three trades and had no open trades, with a stress test result of 5.5719%. The manager noted that the system should not be used as a substitute for an older system used for Ethereum, and that the results may not be reliable due to the small sample size. The manager also noted that missing observations were skipped and not treated as zero returns. The system used data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A test of pre-earnings stock controls for Nokia, which is a company that issues its earnings reports, found that the controls failed in both training and holdout periods. The test looked at 11 instances of morning earnings reports from January 2024 to July 2026, and found that the median absolute change in the stock price was 5.864627%, with a range of -8.474903% to 11.444479%. Two specific options, Sep11S11.13 and Oct30C13.45, needed to increase by 21.293801% and decrease by 17.520216% respectively to be profitable, including assumed roundtrip costs of $0.05 per share. However, neither of these options was profitable, and the test found that no documented option payoff exceeded three times the expected payoff. The test used data from various sources, including Nokia's financial calendar and option contracts. The test also used a trading strategy that bought stocks five days before the earnings report and sold them the day before the report, with a daily adjusted SIP and fees of 1 basis point and slippage of 5 basis points on each side. The test found that this strategy had a return of -14.811928% in the training period and -3.318922% in the holdout period. The test also found that increasing the slippage to 15 basis points resulted in returns of -16.164092% and -3.897272% in the training and holdout periods respectively. A sensitivity test found that changing the parameters of the strategy did not result in any positive returns. A separate test of a momentum-based strategy found a return of 144.635118%, but this strategy was not directly comparable to the original test. The test concluded that the pre-earnings stock controls did not work as intended, and that the results did not validate the use of this strategy for live trading.

A trading strategy was tested on the stock FTK. The strategy had three main rules: first, it only bought FTK if the previous day's closing price was higher than the highest closing price of the last 40 days and the average closing price of the last 50 days; second, it bought FTK at the next day's opening price, with a stop-loss at 2 times the average daily range of the last 20 days below the entry price, and a target price at 6 times the average daily range above the entry price, and it sold FTK after 40 days; third, it accounted for 1 basis point in fees and 15 basis points in slippage for each buy and sell, and it used data from 2020 as a warm-up, from 2021 to 2024 for training, and from 2025 to September 11, 2026, as a hold-out period. The strategy used a long-only portfolio and retained entry-day exits. The results showed 11 closed trades, with no unfinished trades or entry-day exits. The conclusion was to reject the strategy due to a negative geometric return, compared to a buy-and-hold return of 185.580%. The test was not event-specific and did not select parameters from the hold-out period. FTK was selected retrospectively, and corporate or regime changes may weaken the transferability of the results.

A trading strategy was tested on the stock FTK. The strategy had three main rules: first, it only considered buying FTK if the previous day's closing price was higher than the highest closing price of the last 40 days and the average closing price of the last 50 days; second, it bought FTK at the next day's opening price, with a stop-loss set at 2 times the average daily range of the last 20 days below the entry price, and a target price set at 6 times the average daily range above the entry price, and it sold FTK after 40 days; third, it accounted for 1 basis point in fees and 5 basis points in slippage for each buy and sell, and it used data from 2020 as a warm-up, from 2021 to 2024 for training, and from 2025 to September 11, 2026, as a holdout period. The strategy was tested using a long-only portfolio that allowed for two trades per day, and it included fees in the calculation of returns. The test resulted in 11 closed trades, with no unfinished trades or same-day exits. However, the strategy was rejected due to a negative geometric return, compared to a buy-and-hold return of 185.580%. This test was not designed to evaluate the strategy's performance in response to specific events, and it did not involve selecting parameters based on the holdout period or using 15-minute exits or other implementation details. The stock FTK was selected for this test after the fact, and changes in the company or market conditions may limit the applicability of the results.

This is a description of a trading strategy test. The test had three main points: first, it only considered buying a stock if the previous day's closing price was higher than the highest closing price of the last 40 days and the average closing price of the last 50 days; second, it bought at the next day's opening price, with a stop-loss set at 2 times the average daily range of the last 20 days below the entry price, and a target price set at 6 times the average daily range above the entry price, and it sold the stock after 40 days; third, it accounted for 1 basis point in fees and 15 basis points in slippage for each buy and sell, and it used data from 2020 as a warm-up, from 2021 to 2024 for training, and from 2025 to September 11, 2026, as a separate test. The test used a type of moving average called SMA50, which is a way to calculate the average price of a stock over a certain period, in this case, 50 days. A basis point is a unit of measurement, equal to 0.01%, used to express the percentage change in the value of a financial instrument. The test also used a type of risk measurement called ADR20, which stands for Average Daily Range over 20 days, and is a way to measure the average price movement of a stock over a certain period. The source of the data is a website that provides stock market data. The test resulted in 16 closed trades, with 2 of them being closed on the same day they were opened. The conclusion of the test was to reject the strategy because it had a negative geometric return, which means that the strategy lost money over time. For comparison, simply buying and holding the stock would have resulted in a loss of 25.779%. This test was not meant to be used for actual trading decisions, and the stock, FTK, was chosen after the fact, which may not be representative of future results.

This is a description of a trading strategy test. The test had three main points: first, it only considered buying a stock if its previous closing price was higher than its highest closing price in the last 40 days and its 50-day moving average, which is a calculation of the average price of a stock over 50 days. Second, the test entered a trade at the next opening price, with a stop-loss set at 2 times the average daily range of the last 20 days below the entry price, and a target price set at 6 times the average daily range above the entry price, and the trade was closed after 40 trading sessions. Third, the test included fees of 1 basis point, which is a unit of measurement for interest rates and fees, and slippage of 5 basis points, which is the difference between the expected price of a trade and the actual price, on both the buy and sell sides, and used data from 2020 as a warm-up, trained on data from 2021 to 2024, and tested on data from 2025 to September 11, 2026. The test used a type of trading strategy called vectorbt, which is a long-only and full-cash portfolio, meaning it only buys stocks and does not short sell, and it uses two ordered events per day to allow for same-day exits and gap exits at the open. The test also used a type of stop-loss called ambiguous high/low stopfirst. The results of the test included 16 closed trades, with 2 of them being exited on the same day they were entered, and the test concluded that the strategy should be rejected due to a negative geometric return, which is a measure of the return of an investment over time. The test also compared the results to a buy-and-hold strategy, which had a return of -25.779%. The test was not event-specific and did not select parameters from the holdout data, and the stock FTK was selected retrospectively, meaning it was chosen after the fact, and corporate and regime changes may weaken the transferability of the results.

A trading strategy called R91204 was tested on the stock FTK. The strategy had three main rules: first, it only considered buying FTK if the previous day's closing price was higher than the highest closing price of the last 20 days and the average closing price of the last 50 days; second, it bought FTK at the next day's opening price, with a stop-loss set at 2 times the average daily range of the last 20 days below the entry price, and a target price set at 6 times the average daily range above the entry price, and it sold FTK after 40 trading sessions; third, it accounted for 1 basis point in fees and 15 basis points in slippage for each buy and sell, and it used data from 2020 as a warm-up, from 2021 to 2024 for training, and from 2025 to September 11, 2026, as a holdout period. The strategy was tested using a long-only portfolio that allowed for two trades per day, and it included fees in the calculation of returns. The test resulted in 14 closed trades, with 1 trade being closed on the same day it was opened. The strategy was found to have a negative geometric return, which means it performed poorly. For comparison, a simple buy-and-hold strategy would have returned 185.580%. The test was not designed to evaluate the strategy's performance in response to specific events, and it did not involve selecting the best parameters for the strategy or using 15-minute exits. The stock FTK was chosen for the test after the fact, and changes in the company or market conditions may affect the strategy's performance in the future.

A trading strategy was tested on the stock FTK. The strategy had three main rules: first, it only bought FTK if the previous day's closing price was higher than the highest closing price of the last 20 days and the average closing price of the last 50 days; second, it bought FTK at the next day's opening price, with a stop-loss set at 2 times the average daily range of the last 20 days below the entry price, and a target price set at 6 times the average daily range above the entry price, and it sold FTK after 40 days; third, it accounted for 1 basis point in fees and 5 basis points in slippage for each buy and sell, and it used data from 2020 as a warm-up, data from 2021 to 2024 for training, and data from 2025 to September 11, 2026, as a holdout test. A basis point is one hundredth of a percent. The average daily range is a measure of how much a stock's price moves in a day. The test used a type of analysis called vectorbt, which allows for two events per day to keep the same day's exits, and it used a type of stop-loss called gap exits at open, which sells a stock if it opens below a certain price. The test also used a type of stop-loss called ambiguous high/low stopfirst, which sells a stock if it reaches a certain high or low price. The results included 14 closed trades, and the strategy was found to have a negative geometric return, which means it did not perform well. For comparison, a buy-and-hold strategy would have returned 185.580%. The test was not meant to be used for a specific event, and it did not try to find the best parameters using the holdout data. FTK was chosen for the test after the fact, and changes in the company or market conditions may affect how well the strategy works in the future. A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all.

A trading strategy was tested on the stock FTK. The test had three main rules: first, the stock's previous closing price had to be higher than its highest closing price in the last 20 days and its average closing price over the last 50 days; second, the strategy would buy the stock at the next opening price, with a stop-loss set at 2 times the stock's average daily range over the last 20 days, and a target price set at 6 times the average daily range, with the trade closing after 40 sessions; third, the test included fees of 1 basis point and slippage of 15 basis points for each buy and sell, with the data from 2020 used for warm-up, 2021-2024 for training, and 2025 to September 11, 2026, for holdout. A basis point is one hundredth of a percent. The test used a long-only portfolio, meaning it only bought stocks, and it used two events per day to allow for same-day exits and exits at the opening price. The test results showed 26 closed trades, with 2 of them closing on the same day they were opened. The conclusion was to reject the strategy due to a negative geometric return, meaning the strategy lost money. For comparison, simply buying and holding the stock would have lost 25.779%. This test was not meant to predict specific events and did not use the holdout data to select the best parameters. The stock FTK was chosen after the fact, and changes in the company or market conditions might affect the results. A news scan found the data source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all.

A trading strategy was tested on the stock FTK. The test had three main rules: first, the stock's previous closing price had to be higher than its highest closing price in the last 20 days and its average closing price over the last 50 days; second, the trade would start at the next opening price, with a stop-loss set at 2 times the average daily range of the last 20 days below the entry price, and a target set at 6 times the average daily range above the entry price, and the trade would end after 40 trading sessions; third, each trade would have a 1 basis point fee and a 5 basis point slippage on both the buy and sell sides, with the years 2020 used for warm-up, 2021-2024 used for training, and a separate holdout period from 2025 to September 11, 2026. The data for the stock FTK came from the website https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all. The test used a type of trading strategy called vectorbt, which only buys stocks and uses cash for the rest of the portfolio, and it allowed for two trades per day to keep the trades that ended on the same day they started, and it also allowed for trades to end at the opening price if there was a big gap. The test results included the fees paid to start trades in the total returns, and the test passed a check to make sure the returns were calculated correctly. There were 26 trades that ended, and none of them were still open when the test ended, so they were all included in the results. Two trades ended on the same day they started. The test concluded that the strategy did not work well because it had a negative return. For comparison, simply buying and holding the stock would have resulted in a loss of 25.779%. This test was not done to see how the strategy would work in a specific situation, and it did not try different parameters to see which ones worked best. The stock FTK was chosen after the fact, and any big changes in the company or the market could make the results less reliable.

The automated trading fund manager made some premises to test a trading strategy. The premises were: the previous day's closing price was higher than the highest closing price of the previous 10 days and the 50-day simple moving average, which is a calculation to find the average price of a stock over a certain period, in this case 50 days. The manager then entered the trade at the next day's opening price, with a stop loss set at 2 times the average daily range of the previous 20 days, and a target price set at 6 times the average daily range. The trade was exited after 40 sessions. The manager also accounted for 1 basis point fees and 15 basis points slippage on each side of the trade, where basis points are a unit of measurement for interest rates and slippage refers to the difference between the expected price of a trade and the actual price. The data used for the test was from 2020 to September 11, 2026. The manager used a long-only full-cash portfolio, which means the portfolio only buys stocks and does not short sell, and uses two ordered events per day to allow for same-day exits. The results showed 15 closed trades and 1 unfinished trade, which was excluded from the analysis. The conclusion was to reject the strategy due to a negative geometric return, which is a measure of the return on investment. The return was compared to a buy-and-hold strategy, which returned 185.580%. The manager noted that this was not an event-specific backtest and the parameters were not selected from the holdout data. The stock FTK was selected retrospectively, meaning after the fact, and corporate or regime changes may affect the transferability of the results.

A trading strategy was tested on the stock FTK. The strategy had three main rules: (1) buy when the previous closing price was higher than the highest closing price of the last 10 days and the 50-day moving average, which is a calculation of the average price over 50 days; (2) buy at the next opening price, with a stop-loss at 2 times the average daily range of the last 20 days, and a target price at 6 times the average daily range, and sell after 40 trading sessions; (3) consider 1 basis point fees and 5 basis points slippage, which is the difference between expected and actual trade price, on each trade, with data from 2020 used for warm-up, 2021-2024 for training, and 2025 to September 11, 2026, for testing. The test used a type of investment strategy called a long-only full-cash portfolio, which means only buying stocks and holding cash, and used two events per day to allow for same-day exits. The test results showed 15 closed trades and 1 unfinished trade, with 1 trade closed on the same day it was opened. The conclusion was to reject the strategy due to a negative geometric return, which is a measure of return that takes into account the compounding of returns. For comparison, a buy-and-hold strategy would have returned 185.580%. This test was not meant to predict specific events and did not try to find the best parameters for the strategy using the test data. The stock FTK was chosen after the fact, and changes in the company or market conditions may affect the results.

The automated trading fund manager made the following premises: the previous closing price was higher than the previous 10-day high and the 50-day moving average, which is a calculation of the average price over 50 days. The manager entered the market at the next open, with a stop entry at 2 times the average daily range of the previous 20 days, and a target at 6 times the average daily range. The manager exited after 40 sessions. The manager also considered 1 basis point fees and 15 basis points slippage on each side, where basis points are units of measurement for interest rates or fees. The manager used data from 2020 as a warm-up, trained from 2021 to 2024, and used a separate holdout period from 2025 to September 11, 2026. The manager found that the primary L20, which is a type of machine learning model, could not be replaced by a 10/40 sensitivity analysis. The manager used a source of stock bar data from a website. The manager used a long-only full-cash portfolio, which means the manager only bought stocks and did not short sell, and used two ordered events per day to retain entry-day exits and gap exits at open. The manager found that the net returns included entry fees in the denominator, and the portfolio's compounded return reconciliation passed. The manager made 24 closed trades, and excluded 1 unfinished path and 2 entry-day exits from the analysis. The manager concluded that the strategy should be rejected due to a negative geometric return, which is a measure of the return of an investment. The manager compared this to a buy-and-hold strategy, which had a return of -25.779%. The manager noted that this was not an event-specific backtest, and did not select parameters from the holdout period. The manager also noted that the stock FTK was selected retrospectively, and that corporate and regime changes may weaken the transferability of the results.

The automated trading fund manager made the following premises: the fund will enter a trade if the previous day's close is higher than the previous 10-day high and the 50-day simple moving average, which is a calculation of the average price of a stock over 50 days. The fund will enter the trade at the next day's opening price, with a stop loss at 2 times the average daily range of the previous 20 days, and a target at 6 times the average daily range. The trade will be exited after 40 sessions. The fund will also account for 1 basis point fees and 5 basis point slippage on each side of the trade, where a basis point is a unit of measurement equal to 0.01%. The data used for training the model is from 2020, and the model was trained from 2021 to 2024, with a separate holdout period from 2025 to September 11, 2026. The source of the data is a website that provides stock market data. The model uses a long-only full-cash portfolio, which means it only buys stocks and does not short sell, and it uses two ordered events per day to retain entry-day exits and gap exits at the open. The model also uses a system to handle ambiguous high and low stops. The results of the model show 24 closed trades, with 1 unfinished trade excluded from the results, and 2 entry-day exits. The conclusion is to reject the model due to a negative geometric return, which is a measure of the return on investment. The buy and hold return for comparison is -25.779%. This is not an event-specific backtest, and no parameter selection was made from the holdout period. The stock FTK was selected retrospectively, and corporate and regime changes may weaken the transferability of the results.

The trading fund manager extended a prior analysis, called prior2766, by 8 completed 15-minute bars through September 12 at 01:45 UTC, making a total of 48 since September 11 at 14 UTC. The fund had a frozen entry price of 2632.27965, which is the raw price of 2629.65 plus a 10 basis points slip, a stop price of 2456.25266262, and a target price of 3223.79684108, with a 20-day horizon ending on October 1 at 14 UTC. The price moved between a high of 2664.8535 and a low of 2506.24, with the mean touching the stop and target prices plus 2R. The latest price is 2514.15, which means a return of -4.726512%, an estimated liquidation of -5.059731%, and a drawdown of 4.095073%. The vectorbt analysis agrees with the raw wealth within a small margin of 1e-10. No exit occurred, so the win rate is undefined. This analysis was missed before entry and reconstructed later, so it is not prospective performance. The premise is that a cheaper price alone is not a rule to enter, and with no holdings, there is no exit to adjust. The conclusion is to retain the original levels and path as research, without claiming an avoided completed loss or buying retroactively. The existing frozen historical evidence, called 2765, remains and shows an ETH hourly return of 17.2404% with 6 draws and 4 wins out of 6, and a daily return of -17.6325% with 5 losses. A new static analysis, called 2817, is different and not a replacement. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A diagnostic test was run on the stock symbol MU using a momentum strategy, which is not a plan for actual trading. The test used a computer program to look back at 20 days of data and simulate 1095 days of trading, with a 5% stop loss and a 15% target profit. The results showed a 572.77% return, with 44.87% of trades being winners and an average trade profit of 2.77%. The test also showed a maximum loss of 43.83% at some point. This test did not take into account any fees or costs that might occur in real trading, and it is not a recommendation to buy or sell the stock. The stock price of MU is currently too high for the fund to buy a whole share, which would cost more than $49.14. The results of this test should not be used to predict future trading outcomes, and a separate study on the stock symbol CAG showed different results. The data used for this test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run on the momentum strategy for stocks CAG and MU. The test used a 20-day lookback period, 1095 days of data, a 5% stop loss, and a 15% target profit. The results for CAG showed a 6.22% return, compared to a -48.87% return for a buy-and-hold approach. The test also showed a 24.04% maximum drawdown, a 42.86% win rate, and an average trade return of 0.27%. This test is not a strategy for actual trading and does not account for fees or slippage. A separate study on CAG using vectorbt found different results and rejected entry. The test also showed that the trend profit for MU does not necessarily mean there is an edge in earnings run-up, and buying one whole share of MU would exceed the fund's capacity of $49.14. The data for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The results for MU should not be used to predict future performance for a small fund.

A news scan found the headline is not applicable here, instead, a diagnostic analysis was performed on a trading strategy. The analysis used a lookback period of 20 days and 730 days of data to evaluate the performance of several cryptocurrencies, including LTC, HYPE, LINK, UNI, and XRP. The results showed the following returns, drawdowns, wins, and number of trades: LTC had a return of -19.347, a drawdown of 47.221, a win rate of 27.5, and 40 trades; HYPE had a return of -1.692, a drawdown of 19.221, a win rate of 29.41, and 17 trades; LINK had a return of -13.273, a drawdown of 37.201, a win rate of 36.59, and 41 trades; UNI had a return of -15.705, a drawdown of 35.086, a win rate of 32.35, and 34 trades; XRP had a return of 539.028, a drawdown of 19.960, a win rate of 56.52, and 23 trades. The analysis also considered the number of bars, with HYPE having 208 bars and the others having 731 bars, including a partial day. The premise of the analysis is that the gross same-close policy is not a current executable high strategy. Additionally, a comparative analysis of XRP's performance in 2026, with costs included, showed a return of -19.881% and -13.792%, compared to a positive return of 118.38% and 145.31% in 2024-25. The conclusion is to reject extrapolating the gross 539% return into a buy signal, and never to select XRP solely because it won a scan for support. The current hour's close price is 1.360542, which is less than the 480-hour high of 1.54995. The independent bid-ask history and actual 15-minute execution remain unresolved. The full analysis observations are saved, and a comparator contains all cells. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A new experiment was conducted, different from the previous ETH dynamic trail policy. The experiment used fixed prior 15/20/30 day high entries, half-lookback low exits, next exact open, prior completed daily Average True Range (ATR) of 14, 2ATR stop and net 3R target, and a 20-day timeout. A 25 basis point credited asset fee was applied to each side, and 10/20 basis point slippage was used. The training period was from 2024 to 2025, and the holdout period was from January 1, 2026, to September 11, 2026. The current top 10 universe introduces survivorship, meaning that only assets that have survived the previous market outcomes are included. The results show that the static policy differs materially from the frozen +2R management. The results are as follows: for the 20-day baseline daily, the return percentage was 32.83939603366497, the drawdown percentage was 18.886746159805302, and the number of trades was 4. For the training 20-day period, the results were: for the daily timeframe, the return percentage was -38.0179095001473, the drawdown percentage was 64.29387653931362, and the number of trades was 14; for the hourly timeframe, the return percentage was -13.32638118248557, the drawdown percentage was 54.81977390893547, and the number of trades was 15. The stress 20-day period results were: for the daily timeframe, the return percentage was 32.55705116550591, the drawdown percentage was 18.871806489102905, and the number of trades was 4; for the hourly timeframe, the return percentage was -16.83139824947808, the drawdown percentage was 46.396411169802555, and the number of trades was 6. All holdout cells are listed, showing the timeframe, lookback period, slippage, return percentage, and number of trades. The trade-mean bootstrap 10000 IID resamples 95% confidence interval (CI) was calculated, but dependence was not resolved. The conclusion is that this is research only, and no new deployment will be based on this comparator. All 192 cells were recorded as observations. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A new experiment was conducted, different from the frozen ETH dynamic trail policy. The experiment used fixed prior 15, 20, and 30-day high entries, half-lookback low exits, next exact open, prior completed daily Average True Range (ATR) of 14, 2ATR stop and net 3R target, and a 20-day timeout. The experiment also included 25 basis points credited asset fees on each side and 10/20 basis points slippage. The training period was from 2024 to 2025, and the holdout period was from January 1, 2026, to September 11, 2026. The current top 10 universe introduces survivorship, meaning that only assets that have survived the entire period are included. Previous market outcomes were already examined. The results show that the static policy differs materially from the frozen +2R management. The experiment used a 20-day baseline daily with the following results: for the LINK asset, the daily time frame, 20-day lookback, 0.001 slippage, starting on January 1, 2026, the return percentage was 2.4601066893270795, the drawdown percentage was 24.511731635060052, the number of trades was 7, the win rate was 28.57142857142857, the mean trade was 1.1669976748588227, the average win was 21.683702811122462, and the average loss was -7.0396843796466335. The training period results for the LINK asset are as follows: for the daily time frame, the return percentage was -0.5685976465955145, and for the hourly time frame, the return percentage was 42.199504407442156. The stress period results for the LINK asset are as follows: for the daily time frame, the return percentage was 2.208992159505141, and for the hourly time frame, the return percentage was -10.127375734961797. All holdout cells are recorded, including time frame, lookback, slippage, return percentage, and number of trades. The trade-mean bootstrap with 10,000 resamples and 95% confidence interval is also reported. The conclusion is that this is research only, and no new deployment will be based on this comparator. All 192 cells were recorded as observations. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.