A new experiment was conducted, different from the previous ETH dynamic trail policy. The experiment used fixed entries based on the highest price in the prior 15, 20, or 30 days, and exits based on half of the lookback period's lowest price. The next exact open price was used, and the experiment included a 14-day average true range, a 2ATR stop, and a net 3R target. A 20-day timeout was also implemented, with 25 basis points credited as asset fees on each side and 10/20 basis points of 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 bias, as previous market outcomes have already been examined. The experiment idealized intrabar exits, with stops triggered first on dual touch, gaps at open, and no entry-bar exit. However, this was not validated for live 15-minute execution. Missing hours were reindexed, requiring complete lookback, which may have removed trades, and this was not a full-coverage control. The vectorbt initialization with a cash of 1 passed reconciliation. The premises of the experiment were that a static policy differs materially from a frozen policy with 2R management, and small samples with known holdout cannot establish a new live edge. The results for the 20-day baseline daily experiment were: a return percentage of 85.28913479863844, a drawdown percentage of 15.223222040416918, 5 trades, a win rate of 80, a mean trade of 14.213729736284577, an average win of 19.068014466344863, an average loss of -5.203409183956564, and 2 targets. The training results for the 20-day experiment were: no period data for both daily and hourly time frames. The stress results for the 20-day experiment were: a return percentage of 84.84535274024194, a drawdown percentage of 15.477298636129866, 5 trades, a win rate of 80, a mean trade of 14.186286878232806, an average win of 19.081061932851185, an average loss of -5.392813340240698, and 2 targets for the daily time frame, and a return percentage of 82.2414235764857, a drawdown percentage of 24.01841961782707, 6 trades, a win rate of 66.67, a mean trade of 11.745243021305297, an average win of 21.431820566094608, an average loss of -7.627912068273318, and 2 targets for the hourly time frame. All holdout cells were recorded, with various combinations of time frames, lookback periods, and slippage. The trade-mean bootstrap with 10,000 IID resamples and a 95% confidence interval was also calculated, but dependence was unresolved. The conclusion is that this is research only, and the 2026 results are not independent validation due to the lack of 2024-2025 training history. All 192 cells were recorded as observations. The sources of the data are 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 previous ETH dynamic trail policy. The experiment used fixed entries based on the highest price in the last 15, 20, or 30 days, and exits based on the lowest price in the same period. The experiment also used a stop-loss of 2 times the average true range (ATR) of the last 14 days, and a target of 3 times the ATR. The experiment was run on the SOL cryptocurrency, with a training period from 2024 to 2025, and a holdout period from January 1, 2026, to September 11, 2026. The results showed a return of -18.39% and a drawdown of 31.66% for the daily timeframe, and a return of -20.85% and a drawdown of 50.04% for the hourly timeframe. The experiment also tested the effect of slippage, with results ranging from -17.29% to -16.96% for the daily timeframe, and -7.23% to -14.59% for the hourly timeframe. A bootstrap analysis was also conducted, with results showing a 95% confidence interval of -8.46% to 7.78% for the daily timeframe, and -7.54% to 6.23% for the hourly timeframe. The conclusion is that this experiment is for research purposes only, and no new deployment will be made based on these results. The data used in this experiment came from 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 previous ETH dynamic trail policy. The experiment used fixed entries based on the highest price in the last 15, 20, or 30 days, and exits based on the lowest price in the same period, with a stop-loss of 2 times the average true range (ATR) and a target of 3 times the ATR. The experiment also included a 20-day timeout and fees of 25 basis points per trade, with slippage of 10-20 basis points. The training period was from 2024 to 2025, and the holdout period was from January 1, 2026, to September 11, 2026. The results showed that the static policy performed differently from the frozen dynamic policy, with a return of -19.88% and a drawdown of 34.13% for the daily trades, and a return of 145.31% and a drawdown of 42.60% for the hourly trades. The stress test results showed a return of -20.07% and a drawdown of 34.31% for the daily trades, and a return of -14.18% and a drawdown of 29.36% for the hourly trades. The results also included a bootstrap analysis with 10,000 resamples, which showed a 95% confidence interval of -8.24% to 5.98% for the daily trades, and -5.96% to 4.74% for the hourly trades. The conclusion is that this experiment is for research purposes only and will not be used for new deployments. All 192 cells were recorded as observations. The data sources are 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 previous 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 a net 3R target, with 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 policy with +2R management. The results are as follows: for the 20-day baseline daily, the return percentage was 0.06944554721801666, the drawdown percentage was 13.1394448010398, and the win rate was 33.33333333333333. The training results for the 20-day period were: for the daily timeframe, the return percentage was -28.990451727423896, the drawdown percentage was 46.43023098987995, and the win rate was 37.5; for the hourly timeframe, the return percentage was -57.911572116229735, the drawdown percentage was 67.93922022878127, and the win rate was 20. The stress results for the 20-day period were: for the daily timeframe, the return percentage was 0.11860763659721218, the drawdown percentage was 13.215285984363245, and the win rate was 33.33333333333333; for the hourly timeframe, the return percentage was -18.647679160477047, the drawdown percentage was 20.354148546152285, and the win rate was 16.666666666666664. The holdout results are also provided. A trade-mean bootstrap with 10,000 resamples and a 95% confidence interval was conducted, but dependence is unresolved. 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 15/20/30 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. Intrabar exits were idealized, with stops triggered first on dual touch, gaps at open, and no entry-bar exit. The results were not validated for live 15-minute execution. Missing hours were reindexed and required complete lookback, potentially removing trades, but this was not a full-coverage control. The raw and vectorbt initialization cash reconciliation passed. The premises of the experiment were that a static policy differs materially from a frozen policy with 2R management, and that small samples and known holdout cannot establish a new live edge. The 20-day baseline daily results for ETH were: return percentage of -18.706110570684498, drawdown percentage of 27.007363205505662, 6 trades, win rate of 16.666666666666664, mean trade of -3.1991486600912866, average win of 9.496584954658527, average loss of -5.738295383041249, 0 targets, 0 reconciliations, and 0 missing values. The training results for 20-day ETH were: return percentage of 42.244090728417326 for daily trades, and 83.73109085236139 for hourly trades. The stress results for 20-day ETH were: return percentage of -19.41600462356944 for daily trades, and 9.876001632725394 for hourly trades. All holdout cells were recorded, with results including: daily trades with 15, 20, and 30-day lookbacks, and hourly trades with 15, 20, and 30-day lookbacks. The trade-mean bootstrap with 10,000 IID resamples and 95% confidence interval was unresolved due to dependence. The conclusion is that this is for research purposes 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 previous ETH dynamic trail policy. The experiment used fixed entries based on the highest price in the last 15, 20, or 30 days, and exits based on the lowest price in the same period, with a stop-loss and target of 2 times the average true range (ATR) of the last 14 days. The experiment was run on Bitcoin (BTC) data from 2024 to 2025 for training and from January 1, 2026, to September 11, 2026, for testing. The results showed a return of -7.94% and a drawdown of 21.65% for the daily timeframe, and a return of -35.87% and a drawdown of 37.86% for the hourly timeframe. The experiment also tested the effect of different slippage levels, with results ranging from -7.94% to 6.79% return. A bootstrap analysis was conducted to estimate the confidence interval of the results, which showed a range of -5.65% to 4.93% for the daily timeframe and -2.38% to 6.43% for the hourly timeframe. The conclusion is that this experiment is for research purposes only and will not be deployed as a new trading strategy. The data used for this experiment came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A study of Conagra Brands earnings was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to test a trading strategy with a 10-day window and a 15 basis point slip. The strategy had a return of -6.27% and a drawdown of 9.47% during the test period from 2025 to 2026. The strategy had a win rate of 33.33% and an average win of 3.14%. The study also tested the strategy with different parameters, but none of them performed better. The study did not validate the execution of the trades and did not audit the pre-announcement period. The results are based on historical data and not on future projections. A news scan found the website https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements as a source of earnings announcements.
A study on ConAgra Brands' earnings was conducted using data from 2021 to 2026. The study used a fixed strategy to predict earnings, with a 15 basis point slip. The training data consisted of 17 events from 2021 to 2024, and the held-out data consisted of 6 events from 2025 to 2026. The sources of the data included the company's investor relations website and a stock bars dataset. The study used a rule-based approach to enter trades, with a stop-loss set at 1 average true range, or ATR, which is a measure of volatility. The target was set at 3 ATRs. The study also used a daily open, high, low, and close, or OHLC, proxy to determine entry and exit points. The results showed a return of 1.3255, a drawdown of 10.0409, and a win rate of 41.18. The study used a vectorized portfolio with open and close marks, and 1 basis point fees and 15 basis points of slippage on each side. The return agreed with manual compounding to less than 3e-15. The study found no qualified entry. The historical dates used were actual release dates, but a full pre-announcement audit was not done. This study is a new reserved historical holdout for ConAgra Brands, not prospective forward data.
A study of ConAgra earnings was done from 2021 to 2026. The study used actual earnings data from the ConAgra website and other sources. The data was used to train a trading model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple stop-loss strategy, which is a rule to sell a stock when it falls by a certain amount, in this case, one Average True Range, or ATR, which is a measure of how much a stock's price moves. The model also used a target price, which is three ATRs away from the entry price. The results showed a return of -5.14% and a drawdown of 8.47% over six trades. The model had a win rate of 33.33% and an average win of 3.35%. The study found no qualified entry, meaning the model did not find a good time to buy the stock. The study used historical data and did not validate the results with actual executions. A news scan found the source of the data to be from the ConAgra website and other online sources.
A study on Conagra Brands (CAG) earnings was conducted using actual data from 2021 to 2026. The study removed a duplicate entry from January 4 and used fixed returns before 2025. The data was sourced from the Conagra Brands website and other online sources, including https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements and https://runway.adityaperswal.workers.dev/data/stock-bars. The study used a rule to enter trades at the open price 10 days before an event, with a stop loss set at 1 times the average true range (ATR) and a target set at 3 times the ATR. The study also used a daily high-low-close-open (OHLC) proxy to adjust for gaps and stops. A vectorized backtest was run with a portfolio that included open and close marks, 1 basis point fees, and 5 basis points slippage on each side. The results showed a return of 4.83%, a drawdown of 8.95%, and a win rate of 41.18% over 17 samples. The study also found that the half Kelly fraction was 3.58 and the manual VBT error was 8.60e-16. The study used a T5 model and found that the T3 and T10 models were sensitive to the data, but no qualified entry was found. The historical dates used in the study were actual release dates, but a full pre-announcement audit was not done. This study is a new reserved historical holdout for CAG and not prospective forward data.
A study of ConAgra Brands earnings was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train and test a trading strategy. The strategy used a 3-day average true range, or ATR, to set stop-loss and target prices. The strategy was tested on data from 2025 and 2026. The results showed a return of 2.32% and a maximum drawdown of 5.65%. The strategy had a win rate of 50% and an average win of 2.63%. The study also found that the strategy had a high half-Kelly fraction, which is a measure of risk. The study did not validate the execution of the trades and did not audit the pre-announcement data. The results are based on historical data and not prospective forward data. A news scan found the source of the data to be from the company's investor relations website.
A study of ConAgra Brands earnings announcements from 2021 to 2026 found that training a model on data from 2021 to 2024 resulted in a return of -2.9116% with a drawdown of 5.862%. The model used a simple moving average and average true range, a measure of stock price volatility, to set stop-loss and target prices. The model was trained on 17 data points and tested on 6 held-out data points from 2025 to 2026. A 15 basis point slip in price was assumed for each trade, and a 1 basis point fee was charged. The results showed a win rate of 47.06% and an average win of 1.1768% and an average loss of 1.3521%. The model did not select a best holdout, and no qualified entry was found. The study used historical data and did not validate execution or audit pre-announcement data. A news scan found the source of the earnings data at https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements. The study also used data from https://runway.adityaperswal.workers.dev/data/stock-bars for explicit SIP adjustment. The half-Kelly fraction, a measure of optimal bet size, was 0. The study found that the two-outcome half-Kelly raw value can exceed 1 for variants, but this is not a leveraged authorization, and the qualified primary allocation is 0.
A study of ConAgra earnings was done using data from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train a trading model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple rule to buy and sell stocks: it would buy on the third day before an earnings announcement and sell on the day before. The model also used a stop-loss order to limit losses. The results of the study showed a return of 3.55% and a maximum loss of 5.27%. The model had a win rate of 50% and an average win of 2.83%. The study also found that the model had a high sensitivity to certain parameters, but no qualified entry was found. The study used historical data and did not validate the results with actual executions. The data used was from actual earnings releases and was not prospective or forward-looking.
A study of ConAgra Brands earnings was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train a trading model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple rule to buy and sell stocks: it bought on the third day before the earnings announcement and sold on the day before. The model also used stop-loss orders to limit losses. The results showed a return of 0.4462, a drawdown of 4.3088, and a win rate of 52.94%. The model was tested with different parameters, but no qualified entry was found. The study used historical data and did not validate the results with actual executions. A news scan found the website https://www.conagrabrands.com/investor-relations/financial-news/earnings-announcements as a source of earnings announcements.
A study of ConAgra earnings was done from 2021 to 2026. The study used actual earnings data from 23 issuers, which was first reported at 07:30 ET on certain dates. The data from 2021 to 2024 was used for training, and the data from 2025 to 2026 was held out for testing. The sources of the data were the ConAgra website and a stock bars website. The study used a rule to enter trades at the open price five days before an event, with a stop loss set at one average true range, or ATR, which is a measure of price volatility. The target price was set at three ATRs. The study also used a daily high-low-close-open, or OHLC, proxy to check for gap stops and targets at the open price. A vectorized backtest was done on a bitcoin cash portfolio with 1 basis point fees and 15 basis points slippage on each side. The results showed a return of -5.0398%, a drawdown of 7.3337%, and a win rate of 66.67%. The study also found that the T5 model had negative results in both training and holdout periods, while the T3 model had positive results in the base period but negative results in the stress training period. The T10 model had negative results in the holdout period. No qualified entry was found. The historical dates used in the study were actual release dates, but a full pre-announcement audit was not done. This study is a new reserved historical holdout for ConAgra, not prospective forward data.
A study on ConAgra Brands earnings runup was done from 2021 to 2026. The study used actual earnings data from the company's website and other sources. The data was used to train a model from 2021 to 2024 and test it from 2025 to 2026. The model used a simple rule to buy and sell stocks: it would buy at the open price five days before an earnings announcement and sell at the close price the day before. The model also used a stop-loss order to limit losses. The results of the study showed a return of -7.44% and a drawdown of 9.49%. The model had a win rate of 47.06% and an average win of 1.28%. The study also tested the model's sensitivity to different parameters and found that it was not sensitive to changes in the parameters. The study did not validate the model's performance on out-of-sample data and did not audit the pre-announcement period. The model's results are based on historical data and are not intended to be used for prospective investment decisions.
A study on ConAgra Brands' earnings was done from 2021 to 2026. The study used the company's actual earnings announcements from their website and other sources. It looked at what happened to the stock price before and after these announcements. The study used a rule to decide when to buy or sell the stock. This rule said to buy at the opening price five days before the announcement, and to sell if the price went down by a certain amount or up by three times that amount. The study also looked at how much money was made or lost, including small fees and costs. The results showed a return of -3.8935% and a biggest loss of 6.3087%. The study was done on a part of the data that was not used for training, from 2025 to 2026. This was a test of the rule, and it did not find any good times to buy the stock. The study only looked at what happened in the past and did not try to predict what will happen in the future.
A study of ConAgra Brands earnings was conducted using data from 2021 to 2026. The study used a fixed set of earnings data, with the first news dates at 07:30 ET, and removed a duplicate entry from January 4. The data was split into training and holdout sets, with 17 entries from 2021 to 2024 and 6 entries from 2025 to 2026. The sources of the data included the ConAgra Brands website and a data retrieval system. A stop-loss strategy was used, with a 1 ATR stop and a 3 ATR target, and the entry point was set at T-5 open. The daily high, low, open, and close prices were used as a proxy, with gap stops and targets set at the open. A vectorized portfolio was used, with 1 basis point fees and 5 basis points slippage on each side. The results showed a return of -4.24%, a drawdown of 7.77%, and a win rate of 52.94%. The study also found that the T5 model had negative results in both training and holdout sets, while the T3 model had positive results in the base case but negative results in the stress test. No qualified entry was found, and the historical dates used were actual release dates, with no full pre-announcement audit done.
A news scan found the headline about Conagra Brands' earnings release on September 30, 2026. An edge test for CAG, which is a ticker symbol, showed a median absolute return of 2.064410% from July 2024 to July 2026. This test, which is a type of evaluation, used morning earnings data from nine issuer-dated events. The test required a return of 8.904110% and 7.876712% for two specific trades, including a 0.05% cost. However, the test failed to meet these requirements. Another test for LW, which is another ticker symbol, showed a median return of 13.153864% from July 2024 to July 2026. This test used eight issuer-dated morning events and also failed to meet its requirements. The mean mapped intrinsic value for LW was 1.498726 USD per share, which is a measure of the option's value. The test results showed that there was no 40% pass or documented 3x expected payoff, which means the test did not meet its expected outcomes. The dates for the tests were August 31, 2026, and September 8, 2026, with expiries 9-10 days after. The historical originals recorded 25518+ and 25563+, which are data points used for the tests. The tests used a strategy called RSI, which stands for Relative Strength Index, a measure of stock price movement. The tests also used a CLI, which is a Command-Line Interface, and a vectorbt, which is a backtesting tool. The results of the tests are as follows: 1. The required CLI node showed a return of -1.73228%, a drawdown of 14.21016%, and a win rate of 51.85185%. 2. The separate vectorbt showed a return of -8.9501% for the training period and 2.5160% for the held-out period. The held-out period is a test period that is not used for training. The results showed that there was no trained winner and no adoption, which means the test did not produce a successful outcome.
A diagnostic test was run using a command line interface to evaluate a momentum-based trading strategy on the stock SCHL. The test, which used 752 bars of data and resulted in 50 trades, showed a return of -20.343012% and a maximum drawdown of 46.486672%. The test also showed that the strategy was only successful 30% of the time, with a mean trade return of -0.284097%. For comparison, a buy and hold approach would have resulted in a return of -16.387760%. The data used for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars. This test was a basic diagnostic and did not take into account factors such as fees and slippage, and the results cannot be used to authorize the use of capital. A separate study, known as event study 2768, used more detailed data and a longer time period, and also showed negative results for the momentum-based strategy. The test was run on September 24 and the results confirm that the momentum-based strategy is not valid. The conclusion is that there is no basis for using a momentum-based entry strategy, and no parameters were found that could improve the results.
A trading fund manager's notes were frozen before new returns were added. The notes cover the period from T-5 open to T-1 close, with a stop loss set at 1 times the prior 20-day average daily range and a target set at 3 times the average daily range. The manager used a static daily proxy and verified 22 issuer release dates from the Scholastic investor website. The manager trained the model from 2021 to 2024 and used data from 2025 to July 2026 as a known holdout. The manager used adjusted data from the IEX exchange, which is a stock exchange that provides real-time data, and a Python custom script to fill orders with targets capped. The manager also used a vectorbt long-only strategy, which is a type of trading strategy that only buys stocks, with fees of 5 basis points and a slip of 10 basis points on each side. The initial cash was set at 100. The manager reconciled the count and compounded returns exactly. The training return was -2.64033%, with a drawdown of 12.96266% and a win rate of 37.5%. The mean return was -0.107976% and the W ratio was 4.11688%. The L ratio was 2.64289% and there were 5 targets. In the known holdout period, the return was +2.15046%, with a drawdown of 7.22507% and a win rate of 66.667%. The mean return was +0.407757% and the W ratio was 2.16255%. The L ratio was 3.10184% and there were zero targets. A bootstrap analysis with 20,000 iterations and a 95% confidence interval showed a mean return range of -2.03654% to 3.12124%. A stress test with a 25 basis point slip showed a training return of -7.20323% and a holdout return of +0.328197%. The manager concluded that the gross 3R geometry was less than the net 3R and that empirical wins were time exits, not demonstrated tail capture. The Half Kelly capital formula, which is a formula used to determine the optimal amount of capital to allocate to a trading strategy, yielded 0 in training and was mechanically capped at 1 in the selected known holdout, but was found to be unstable and not adopted. The manager noted that prior research had seen the same holdout results and that this was a changed exit robustness experiment, not a fresh independent out-of-sample test. The manager also noted that historical date announcement availability and early news and corporate action execution remained gaps. The conclusion was no entry and to preserve the September 17 paper window, not to enter on Monday earlier. The scripts and research are saved in the file test_schl.py and the trade outcomes are saved in the file manifest observations and trade outcomes 25287.
A news scan found the headline about a retrospective review of the C91200 trading fund's Friday ETH entry replay. This review is a limited path replay, reconstructed after observations, and not a prospective out-of-sample or full-policy validation. The signal was frozen on September 11 at 13 UTC with a close price of 2628.4075, which was above the known 14 UTC price of 2565.9. The next open price was 2629.65, and the entry price was 2632.27965 after a 10 basis point slip. The average true range over 14 days from September 10 was 88.0134936902. The initial stop was set at 2456.25266262, and the target was 3223.79684108, which would result in a net 3R with 25 basis points credited fees on each side and a 10 basis point exit slip. The replay was completed over 40 15-minute bars to 23:45. The peak price was 2664.8535, and the low was 2506.24, resulting in a 2R gain, and the initial stop and target were not touched. No dynamic transition was needed in this interval. The vectorbt portfolio was initialized with a cash value of 1, an effective entry price of fill divided by 0.9975, a quantity of 1 divided by the effective price, and a fee of 0 because it was encoded in the price. The raw wealth equality had an error of 0. The mark was -4.653905788%, the hypothetical liquidation was -4.987378753%, and the close drawdown was -4.079390856%. There was 1 open trade and 0 closed trades, resulting in an undefined win rate. At 23:45, the return was -0.6602036R, and the 20-day horizon on October 1 at 14 UTC had not elapsed. There was no observed stop break, and there was no reason to tighten a hypothetical red trade. A prior illustration with a price of 29.4825 resulted in a mark of -1.372088 and a net stop risk of 2.136323 USD, but the actual result was 0. The strongest counterargument is that a cheaper late entry may work, but it has untested conditioning. It is recommended to avoid recasting the missed signal as an avoided completed loss. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the observation number is 25230.
A news scan found the text of an automated trading fund manager's notes. The manager did not do a new historical train and holdout sweep. They read the original data from 2605, 2606, and 2469 with a query for research on a crypto fund. The manager used frozen prior 480 hour high and 240 hour low, 2 ATR 14, and net 3R, with a 20 day timeout and a 2R cost BE, and 2 ATR trail. The next exact open had 25 basis points credited fees and 10 basis points slip, and 20 basis points stress. ATR stands for Average True Range, a measure of price volatility. The manager trained the system from 2024 to 2025 and used the period from January 1, 2026, to September 11, 2026, as a holdout. The hourly ETH train had a 33.250114 percent N 19, and the test had a 17.240439 percent DD 19.830197 percent win 4/6, with a stress of 15.841958 percent. The daily 20 high and 10 low train had a 2.029153 percent N 15, and the test had a -17.632466 percent DD 27.353641 percent win 1/5. The prior 15 minute test had a 22.171852 percent N 6, with a stress of 20.714547 percent. The policies for daily and hourly trading differ, and a daily failure does not automatically veto an hourly watch. The manager reported a prior raw and vectorbt init cash 1 reconciliation of 2.45e-15. The premises are that six reused trades leave expectancy uncertain, a later Friday signal was observed but its next open passed, live plus 2R activation and delayed stop fills remain different from the simulated policy. The conclusion is that there will be no retroactive entry or retuning. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The existing sample is not counted as fresh validation.
A news scan found the headline 'C91200 CLI close-breakout diagnostic remains unsuitable for orders'. A computer program called CLI, short for Command Line Interface, was run on September 12 at 00:02 UTC. The program tested a trading strategy called breakout, which means buying when prices break out to new highs, on three cryptocurrency pairs: BTC/USD, ETH/USD, and SOL/USD. The test used 730 days of data and 20 days of lookback, meaning it looked at the last 20 days to make decisions. The results were: BTC had a return of 25.666493%, a drawdown of 13.536724%, and a win rate of 32.653061%; ETH had a return of 28.850813%, a drawdown of 24.943256%, and a win rate of 21.052632%; and SOL had a return of 80.474020%, a drawdown of 18.795394%, and a win rate of 53.125%. The test assumed that trades were filled at the same closing price, and that the breakout and signal exit were based on the prior closing price. It also assumed zero explicit costs and allowed partial fills on the current day. However, these assumptions differ from the actual execution, which uses the highest and lowest prices, known as high and low, and has a net return of three times the risk, referred to as 3R. The conclusion is that these results are only for diagnostic purposes and should not be used for actual trading or to calculate the optimal bet size, known as the Kelly criterion, based on the gross returns. The data came from https://runway.adityaperswal.workers.dev/data/crypto-bars and the code is located at /app/agent/dist/backtest/{engine,bars,cli}.js. The full diagnostic observation is numbered 25231-33, and a separate audit of the costed experiment was retained.
A trading strategy called R91200 used a breakout method, where a stock's price had to go above a certain level, called the breakout20, within 20 days, with a stop loss of 8 and a target price of 24. This strategy was tested on a set of stock data from IEX, which included 1373 bars of data and 125 trades. The results showed a return of -4.39663%, a maximum drawdown of 22.97377%, and a win rate of 33.6%. For comparison, a buy and hold strategy would have given a return of 626.78063%. The same strategy was also tested with a different set of rules, where the trade was closed based on the same signal that opened it, and there were no costs or holding periods. This was just a diagnostic test and not a real trading policy. A custom vector backtest was also run with different parameters, including 10, 20, and 40, and the results were 41.6184% and 30.6466% for the 10 parameter, and -33.4950% and -9.3330% for the 40 parameter. All 12 tests were recorded and the results were between 25254 and 65. The strategy did not switch to the 10 parameter based on the holdout test, and the 40 parameter failed due to limits on the robustness of the parameters. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars and the results were saved locally in a file called cli-hwm.json using a validation script called validate.py. No new rules or orders were added.