Stats

The primary lookback period, L20, remains fixed. The L10 and L40 lookback periods are used for sensitivity analysis only, with no selection of the best lookback period after reviewing the holdout data. The split-only analysis excludes dividends, so it only considers price performance, not total shareholder return. Changing the adjustment also changes the signal dates and exits. The analysis uses the same parameters: 2 ADR stop, 3R target, 40 sessions, 1 basis point fees, and 5/15 basis points lip. The vectorbt open, execution, and close marks are used, with training data from 2021 to 2024 and testing data from 2025 to September 11. The results, which exclude open marks from the win percentage, are as follows: a list of results with different lookback periods, adjustments, start dates, and slip values, including return, drawdown, win percentage, and number of trades. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All exact means, confidence intervals, and exit counts were recorded from 26226 to 26265. The test period is shared across variations, so the variations are not independent evidence.

The primary lookback period, L20, remains fixed. The L10 and L40 lookback periods are used for sensitivity testing only, with no selection of the best lookback period after reviewing the holdout data. The split-only method excludes dividends, so it only measures price performance, not the total return to shareholders. Changing the adjustment also changes the signal dates and exits. The same parameters were used for all tests, including a 2ADR stop, 3R target, and 40 sessions, with fees of 1 basis point and slippage of 5 or 15 basis points. The vectorbt open, execution, and close marks were used. The data was trained from 2021 to 2024 and tested from 2025 to September 11. The results, which exclude open marks from the win percentage, are as follows: a list of tests with different lookback periods, adjustments, start dates, slippage, returns, drawdowns, win percentages, and number of trades. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All exact means, confidence intervals, and exit counts were recorded from 26226 to 26265. The test period is shared across all variations, so the variations are not independent evidence.

A trading fund manager analyzed the stock STNG. Before running the analysis, the manager set rules: if the stock's price broke out from its prior 20-day close and 50-day Simple Moving Average, which is a calculation of the stock's average price over 50 days, the manager would buy the stock at the next session's open. The manager would set a stop loss, which is an order to sell the stock when it falls to a certain price, at two times the stock's prior 20-day Average Daily Range, which is a measure of the stock's volatility. The target price, which is the price at which the manager wants to sell the stock, was set at three times the initial risk. The manager would hold the stock for a maximum of 40 sessions, including the entry session. The manager also set a rule to sell a quarter of the stock if it reached a 2R return, which means the stock's price increased by twice the initial risk, and make the stop loss break even the next day. The manager tested this strategy using historical data from September 11, 2025, and found that the return was 36.36363636363637% for winning trades, excluding open outcomes. The manager also found that the strategy had 1 target, 7 stops, and 3 timeouts, and that buying and holding the stock would have resulted in a 74.95342579176152% return. The manager used a dataset from https://runway.adityaperswal.workers.dev/data/stock-bars, which provides synthetic dividend-adjusted asset prices, not cash dividend payment timing or executable raw-price fills. The manager separated the data into training and test periods and found that the strategy had an uncertainty of -4.524399609507263% to 5.843595649618188% based on 10,000 bootstrap resamples. The manager notes that this analysis is not proof of a zero edge, which means that the strategy may still be profitable, and that the results should not be treated as an established stock edge due to holdout and stress weaknesses.

A trading fund manager's notes were analyzed. The manager used a system that froze certain settings before running the results. These settings included a breakout and a 50-day Simple Moving Average, which is a way to calculate the average price of a stock over a certain period. The system also used a stop-loss, which is a price level at which to sell a stock to limit losses, set at twice the average daily range of the past 20 days. The target price was set at three times the risk, and the maximum number of sessions was 40, including the entry session. The system was tested on the stock STNG. If the stock reached a certain level of profit, called 2R, the system would sell a quarter of the stock and make the stop-loss price equal to the purchase price the next day. The system used a 1 basis point fee and 5 basis points of slippage per side, which is a way to measure the cost of buying or selling a stock. The results showed a return of 44.44444444444444% and a mean bootstrap return of between -1.5149595070621042% and 8.708828524196868%. The system had 6 targets, 14 stops, and 7 timeouts. The data was retrieved from a website on September 12. The system's results are based on a model that adjusts for dividends, but does not reflect the actual timing of dividend payments or the prices at which trades can be executed. The test period was separate from the training period, but the test period had been previously inspected for other rules, so it was not completely unseen. The results show the payoff shape, but do not reflect the whole-share or 15-minute implementation. The holdout and stress weakness prevent the screen from being treated as an established stock edge.

A news scan found the results of a trading simulation for the stock STNG. The simulation was frozen before the results were generated, using a prior 20-day close breakout and a 50-day Simple Moving Average, which is a calculation of the average price of a stock over a certain period of time. The next session's open price was used, and the stop loss was set at 2 times the prior 20-day Average Daily Range, which is a measure of the average daily price movement. The target was set at 3 times the risk, and the maximum number of sessions was 40, including the entry session. The simulation used a prior 2020 warm-up period. The exits were fixed, with no discretionary trailing, which means that the exit points were predetermined and not subject to change. Gap exits used the open price, and ambiguous daily bars stopped at the first price, with entry day exits allowed, although none were observed. The costs included a 1 basis point fee and 5 basis points of slippage per side, with a stress test of 15 basis points of slippage. The vectorbt long-only fractional full-equity portfolio was reconciled to manual cash, including the entry fee, and the marks were ordered by open, execution, and close. The return included 1 open mark, with 11 losses and a win rate of 27.27%. The mean bootstrap of 10,000 independent and identically distributed resamples, with a seed of 91206, showed a 95% confidence interval of -5.20% to 5.85%, which indicates uncertainty and not proof of zero edge. The results showed 1 target, 7 stops, and 3 timeouts, with a buy and hold return of 74.95%. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars, retrieved on September 12, with a timeframe of 1 day and an adjustment for all dividends. The adjustment all returns model used synthetic dividend-adjusted assets, not cash dividend payment timing or executable raw price fills. The training and test periods were separated, and the test period had previously been inspected for other rules, making this a chronological holdout for this frozen specification, but not globally unseen evidence. The results quantify the payoff shape, but not the whole share or 15-minute implementation. The holdout and stress weakness prevent treating the screen as an established stock edge.

A trading fund manager analyzed the stock STNG from 2021 to 2024. The manager used a fixed exit strategy, which means they decided when to sell the stock before the results were known. The strategy was based on the stock's prior 20-day closing price and its 50-day moving average, which is a way to smooth out price changes. The manager also set a stop-loss, which is a price at which to sell the stock if it falls, at 2 times the stock's average daily range over the past 20 days. The target price was 3 times the risk, and the manager limited the trade to 40 sessions, including the day the stock was bought. The manager used a long-only strategy, which means they only bought the stock, and they reconciled the portfolio to manual cash, including the entry fee. The return on investment was calculated, excluding open outcomes, and the win rate was 46.15384615384615%. The manager also ran a simulation 10,000 times to estimate the uncertainty of the results, which showed a 95% confidence interval of -0.6130553619346064% to 10.616290391886833%. The results showed 6 targets, 12 stops, and 8 timeouts, and the buy-and-hold return was 377.8375088751394%. The data used was from the website https://runway.adityaperswal.workers.dev/data/stock-bars, retrieved on September 12, and the adjustment used was all, which means the returns were adjusted for dividends and other factors. The training and test periods were separated, and the test period was previously inspected for other rules, so the results are not necessarily representative of the whole market. The manager notes that the results do not prove a zero edge, but rather quantify the payoff shape, and that the holdout and stress weakness prevent treating the screen as an established stock edge.

A trading fund manager's notes were frozen before the results of this run. The notes show that the fund used a prior 20-day close breakout and a 50-day Simple Moving Average, which is a calculation of the average price of a stock over 50 days. The next session's open price was used, and the stop loss was set at 2 times the prior 20-day Average Daily Range, which is a measure of the average daily price range of a stock over 20 days. The target was set at 3 times the risk, and the maximum number of sessions was 40, including the entry session. The fund also used a prior 2020 warm-up period. On a surviving day when the fund reached 2 times the risk, it sold a quarter of the position and made the stop loss breakeven the next day. The fund tested the full stop loss and target first, so intraday ordering and 15-minute management were not reconstructed. Gap exits used the open price, and ambiguous daily bars stopped first. Entry day exits were allowed, but none were observed. The fund had costs of 1 basis point fee and 5 basis points slippage per side, and a stress test of 15 basis points slippage. The vectorbt long-only fractional full-equity portfolio was reconciled to manual cash, including the entry fee, and marks were ordered open, execution, and close. The return included 1 open mark, and the fund had 18 losses and a win rate of 44.44444444444444%, excluding open outcomes. The mean bootstrap of 10,000 independent and identically distributed resamples with a seed of 91206 showed a 95% confidence interval of -2.240298785942834% to 8.466477223104828%, which is a measure of uncertainty, not proof of zero edge. The fund had 6 targets, 11 stops, and 1 timeout, and the buy and hold return was 280.5877803557618%. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, retrieved on September 12, and the adjustment all returns model used synthetic dividend-adjusted assets, not cash dividend payment timing or executable raw-price fills. The training and test periods were separated, and the test period was previously inspected for other rules, so this was a chronological holdout for this frozen specification, but not globally unseen evidence. The result quantifies the payoff shape, not the whole-share or 15-minute implementation. The positive result retains the watch, but the upcoming raw stop distribution mechanics must be resolved.

A news scan found the report of an automated trading fund manager for the stock FRO. The manager's rules were: if the stock's price breaks out above its prior 20-day close and 50-day Simple Moving Average, which is a calculation of the stock's average price over 50 days, then buy the stock at the next session's open. The stop loss, which is the price at which to sell the stock to limit losses, was set at 2 times the stock's prior 20-day Average Daily Range, which is a measure of how much the stock's price typically moves each day. The target price, which is the price at which to sell the stock for a profit, was set at 3 times the amount risked. The manager also decided to sell one quarter of the stock if it reached a 2R profit, which means twice the amount risked, and to make the stop loss break even the next day. The manager tested these rules using historical data from September 11 to September 12, and found that the strategy had a return of 42.42424242424242% excluding open outcomes, with 33 losses and 7 targets reached out of 24 stops and 2 timeouts. The manager also found that buying and holding the stock would have resulted in a return of 191.44447020635292%. The manager used a mean bootstrap of 10000 resamples to estimate the uncertainty of the results, and found that the 95% confidence interval was between -1.7832491318441552% and 5.371609867705768%. The manager retrieved the historical data from https://runway.adityaperswal.workers.dev/data/stock-bars on September 12 at 23:40Z.

A trading fund manager made notes about a trading strategy. The strategy was frozen before the latest results came in, based on a prior 20-day close breakout and a 50-day simple moving average, or SMA, which is a way to smooth out price changes. The next session's open price was used, and the stop loss was set at 2 times the prior 20-day average daily range, or ADR, which is a measure of how much prices move. The target was set at 3 times the risk, and the maximum number of sessions was 40, including the entry session. The strategy was backtested using data from 2020 as a warm-up period. The strategy used full fixed exits, with no discretionary trailing stops, which means the stop loss was not adjusted manually. Gap exits used the open price, and ambiguous daily bars stopped at the first sign of trouble. Entry day exits were allowed, but none were observed. The strategy included costs of 1 basis point fee plus 5 basis points of slippage per side, which is a measure of how much prices move when a trade is made, and stress tested with a 15 basis point slippage. The portfolio was reconciled to manual cash, including the entry fee, and marks were ordered by open, execution, and close. The return included 1 open mark, and the win rate was 47.05882352941176%. The mean bootstrap return, which is a way to estimate uncertainty, was calculated using 10,000 independent and identically distributed resamples with a seed of 91206, and the 95% confidence interval was -1.3523231382960088% to 10.722254655961946%, which is a measure of uncertainty, not proof of zero edge. The strategy had 7 targets, 9 stops, and 1 timeout, and the buy and hold return was 280.5877803557618%. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FRO&timeframe=1Day&feed=sip&adjustment=all, retrieved on September 12 at 23:40Z. The adjustment all returns model used synthetic dividend-adjusted asset prices, not cash dividend payment timing or executable raw price fills. The training and test periods were separated, and the test period had previously been inspected for other rules, so this was a chronological holdout for this frozen specification, but not globally unseen evidence. The strategy quantifies payoff shape, not whole share or 15-minute implementation. A positive result retains watch, and the upcoming raw stop distribution mechanics must be resolved.

A trading fund manager has written notes about a training program for the stock FRO. The program uses a fixed exit strategy, which means it sells stocks at a set price. The strategy is based on two things: the stock's price compared to its average price over 50 days, and its previous day's closing price. If the stock's price is higher than these two things, the program buys it. The program also sets a stop loss, which is a price at which it automatically sells the stock if it starts to lose value. The stop loss is set at twice the stock's average daily range over 20 days. The program aims to make three times the amount it risks, and it limits the time it holds a stock to 40 sessions. The program was tested using data from 2020 and 2021. The test included costs such as a 1 basis point fee and 5 basis points of slippage per trade. The results showed a return of 33.33333333333333% of winning trades, excluding trades that are still open. The program used a statistical method called bootstrapping to estimate its uncertainty, which showed a 95% confidence interval of -1.9720576853637237% to 6.135799986935027%. The program had 9 successful trades, 21 stopped trades, and 3 trades that timed out. The stock FRO had a return of 191.44447020635292% if it was bought and held. The data used for the test was retrieved from a website on September 12. The program adjusts the stock's price to account for dividends, but it does not account for the timing of dividend payments or the actual price at which trades are executed. The test was done in two parts: a training period and a test period. The test period was not used to develop the program, but it was used to test other rules. The program's results are positive, but it needs to be improved to handle stop losses in a more realistic way.

An edge test was conducted on ten issuer-dated scheduled AMC earnings from March 2024 to June 2026. The test found that the daily pre-release close to next-session close had a median absolute change of 4.700378% and a signed mean change of -3.027709%. As of September 11, the S2.53 and October 16 K2.5 call and put prices were.21 and.16, respectively, with assumed roundtrip costs of.05 per share, requiring a 9.090909% and -9.486166% change, respectively. Both of these changes are below 40%. The old frozen N9 remains at 1/9, with a median of 5.494505%, and will be preserved separately. There is no documented expected option payoff. The source of the data is option_contracts and https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-bars. The issuer announced on August 25 that financial results will be released after financial markets close on Monday, September 14, 2026. The October 16 expiry is 32 days after this date. The hurdles exceed the new median, and there is no hold exception. The closes are not executable asks, and the intrinsic value is not the actual option profit or loss. The test used a vectorbt926SIPallbarsJan2023–Sep11 2026, buying at the T-2/3/5 US session open and exiting at the T-1 close before the AMC date, with an initial investment of 1000, fees of 10 basis points, and slippage of 10 basis points each side, and stress of 30 basis points lippage. The results of the test are as follows: the T5 train had a return of 3.867305%, a drawdown of 11.915644%, and a win rate of 42.857143%, while the holdout had a return of -2.631698%, a drawdown of 5.363985%, and a win rate of 66.666667%. The stress holdout had a return of -3.793140% and a win rate of 33.333333%. The corrected train T3 now has the highest return of 4.438587%, but the holdout has a return of -4.762379% and the stress holdout has a return of -5.898406%. The model selection is unstable. The T2 train had a return of 3.842905%, the holdout had a return of 2.134481%, and the stress holdout had a return of 0.916186%. The required CLI momentum 20/days 1000 diagnostic had 685 bars, with a return of 26.210635%, a drawdown of 44.737554%, and a win rate of 29.629630%. The buy and hold return was 55.246914%. There is no historical option return, drawdown, or win rate available, and there is no options order from stock proxy. The costs are assumed rather than measured, and the daily close execution proxy does not validate 15-minute option stops.

A news scan found the headline about a diagnostic test called C91204 CLI. The test was run on a computer program using the command: node /app/agent/dist/backtest.js --symbols BTC/USD,ETH/USD,SOL/USD --strategy breakout --lookback20 --days980. This means the program looked at the prices of Bitcoin, Ethereum, and Solana over 980 days, using a strategy called breakout, which is a type of trading strategy that involves buying when the price breaks out of a certain range, and looked back 20 days to make decisions. The results were: for Bitcoin, 46.46% return, 14.69% maximum loss, and 34.33% of trades were winners; for Ethereum, 40.80% return, 24.94% maximum loss, and 30.91% of trades were winners; and for Solana, 72.15% return, 18.79% maximum loss, and 52.78% of trades were winners. For comparison, if you had just bought and held these currencies, the returns would have been: 75.66% for Bitcoin, 12.12% for Ethereum, and -35.31% for Solana. The program used a policy that looked at the closing prices of the last 20 days to make decisions, and did not include any fees or holdout periods. This diagnostic test is different from a validation test, and the results should not be used to choose which currency to trade. The data used for this test came from https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the text describing the C91204 coherent daily reconstruction. This reconstruction uses eight predefined sensitivity cells to rebuild the daily high, low, open, and close prices of Ethereum, or ETH, from its 24 hourly prices. If a day is missing some hours, it is marked as incomplete and becomes unavailable, or NA, because the system requires a full 20-day history of the average true range, or ATR, and the highest price, or HIGH. The original files and 15-minute managed execution are kept. The first premise is that there are differences between the historical daily and hourly prices: on December 2, 2025, the ETH open price was 3020.1375, but the hourly aggregate was 2798.84, which is a difference of -7.3274 percent. In 2026, all 253 complete ETH days had matching daily high, low, open, and close prices. The second premise is that changes in the daily input data around a missing day in March affect the availability of the data, but this is a change in the data or control, not in the performance, or alpha. The hourly baseline remains unchanged at 42.312262 percent return, 37.521903 percent drawdown, 7 wins out of 13, and 1 open trade. The holdout set had a return of 19.206747 percent, a drawdown of 14.542745 percent, 3 wins out of 3, and 1 open trade. The stress holdout set had a return of 17.549943 percent. The daily holdout set changed from -0.292829 percent to -1.925730 percent, and the stress holdout set changed to -2.707198 percent. The daily training set remained unchanged at -27.126920 percent return. The conclusion is that issues with aggregating the data do not explain away the positive results of the hourly candidate. However, the daily negative results, cell selection, small sample size, and non-pristine holdout set still limit the deployment. All eight cells were recorded, bringing the total policy comparators to over 200, plus CLI3 diagnostics, but these are not 200 independent strategies or events. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars and the C91204aggregate_control.py file.

A news scan found the headline 'C91204 missing-checkpoint bounds preserve ETH modeled holdout; literal complete coverage remains zero'. This refers to an analysis of Ethereum trades from March 5-25, April 9-29, and July 7-27, which had 6, 3, and 9 missing 15-minute checkpoints, totaling 18. The term 'checkpoint' refers to a point in time when trade data is recorded. Despite these missing checkpoints, the analysis found that the primary high and low bounds excluded all initial stops, net 3R targets, and gross +2R touches. For example, on March 12 at 17 hours, four missing points used the completed day range of 2018.698-2094.2115, compared to the trade stop of 1791.83424 and +2R target of 2603.77278. The analysis also found that no imputed prices or zero returns were used. The condition that trade bar extrema covered missing intervals meant that all 18 missing checks could not change the three trade paths. The modeled holdout resulted in a 19.206747% return, with a 14.542745% drawdown and 3 out of 3 winning trades, and 1 open trade retained. However, a strong counterargument is that broader trade bars are not historical executable quotes and do not prove fills, check availability, or infinite liquidity. The training mean confidence interval still crosses 0. The validity of the analysis is that the bound proof supports no modeled threshold touch, not global data quality or trading edge. In conclusion, the analysis suggests that literal complete coverage alone should not be cited as a rejection, and the candidate should be kept prospective and separate from fresh entry gates. The full bounds recorded observations can be found in C91204gapbounds.py, sourced from https://runway.adityaperswal.workers.dev/data/crypto-bars.

A diagnostic and sensitivity audit was performed on a trading strategy using the command line interface. The command used was to run a node with the following parameters: the file backtest.js, the symbol FTK, the breakout strategy, a lookback period of 20 days, a total of 2080 days, a stop loss percentage of 10, and a target percentage of 30. The results showed a return of 13.7774%, a drawdown of 49.9074%, and 66 trades with a win rate of 33.3333%. This test did not account for costs or adjustments and was not an independent holdout. The data used can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. A sensitivity audit was also performed with different parameters, including a 10, 20, and 40 period sensitivity, using a SIP adjustment, a warmup period in 2020, training from 2021 to 2024, and a holdout period from 2025 to September 11, 2026, with fees of 1 basis point and a slip of 5 or 15. All 12 tests had negative results. The correct open and close marks were used, superseding earlier drawdown only marks. The training drawdown for the 10 period sensitivity was 86.7031% and 87.1476%, while the holdout drawdown was 46.9088% and 47.8559%. The training drawdown for the 40 period sensitivity was 59.3906% and 60.6103%, while the holdout drawdown was 31.7072% and 32.8585%. The sample size for the 10 period sensitivity was 24 trades for training and 15 trades for holdout, each with 1 unfinished trade. The sample size for the 40 period sensitivity was 16 trades with no change in returns from 2847 to 2850 and 2855 to 2858. There was no favorable lookback selection or strategy adoption. The command line interface sample of 66 unadjusted trade histories and SIP costed samples answer different questions.

This update corrects the marks for FTK as of 2025-01-01 with a 15bp change. It only changes the drawdown in the main analyses, which are sections 2851-2854, and keeps the rule from 2531 that freezes certain values. The original marks used the closing prices for both parts, which incorrectly marked the exit day close before the modeled intraday exit. The corrected marks use the actual open and close prices, alternating between them, with entry happening in the first slot and exit in the second slot. The drawdown marked by open and close is not the full worst-case scenario within a day. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all. The strategy uses a close breakout with a 20-period limit and a 50-period simple moving average, with the next open, a 2 average daily range stop, and a 3 times return target, over 40 sessions, with 1 basis point fees and 15 basis points slip per side, from 2020 for warm-up, and from 2025-01-01 to 2026-09-11. There were 14 completed trades and 0 open trades; the strategy stops at the first ambiguous bar, allows gap open exits, and retains entry day exits. The compounded cash return reconciliation passed. The strategy still does not allow trading due to the absence of 15-minute management and a plus 2 times return management. All 12 sensitivity revised drawdown tables have been updated in the latest observation without retuning any parameters.

This correction replaces the previous drawdown analysis in sections 2851-2854, but keeps the rule that was in place since 2531, including samples, fills, and returns. The original analysis used closing prices for both entry and exit points, which incorrectly marked the exit day close before the modeled intraday exit. The corrected analysis uses actual open and close prices, alternating between the two, with entry occurring at the start of the period and exit at the end. This means that the drawdown, which is the decrease in value, is not the full worst-case scenario for the entire day. The data comes from a stock market data source. The trading strategy used is based on a 20-day closing price breakout and a 50-day moving average, with a stop loss and target return. The strategy was tested from January 1, 2025, to September 11, 2026, with 14 completed trades and no open trades. The analysis still does not recommend trading due to missing risk management and other factors. All sensitivity analyses have been revised in the latest observation, with no changes to the parameters.

This update replaces the previous drawdown information in analyses 2851-2854, but keeps the rule that was in place since 2531, along with samples, fills, and returns. The original marks used closing prices for both entry and exit, which incorrectly marked the exit day close before the modeled intraday exit. The corrected marks now alternate between actual open and close prices, with entry happening in slot 0 and exit in slot 1. This means the drawdown marked by open and close prices does not show the full worst-case scenario that could happen within a day. A news scan found the data source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all. The trading strategy used is L20 close breakout with a 50-day Simple Moving Average, next open, 2 Average Daily Range stop, and a 3 Reward target, over 40 sessions, with 1 basis point fees and 15 basis points slip per side, from 2020 warmup to 2021-01-01 to 2024-12-31. There were 26 completed trades and 0 open trades. The strategy stops at the first ambiguous bar, retains gap open exits and entry day exits. The compounded cash return reconciliation passed. However, the strategy still rejects trading due to the absence of 15-minute management and a +2 Reward management. All 12 sensitivity revised drawdown tables have been updated in the latest observation without any parameter retuning.

A news scan found the text: Supersedes drawdown only in primary analyses 2851-2854; retains frozen 2531 rule, samples, fills and returns. Original two-slot marks used closing prices in both slots, incorrectly marking exit-day close before modeled intraday exit. Corrected vectorbt, a backtesting library, price marks alternate actual open/close, while entry occurs slot 0 and exit slot 1; open/close marked drawdown is not full intraday worst case. Source is a data line from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all. The strategy uses L20 close breakout and SMA50, next open, 2 ADR stop, and 3R target, with 40 sessions, 1bp fees, and 5bps slip per side, from 2020 warmup, 2021-01-01 to 2024-12-31. There were 26 completed trades and 0 open trades; stop first ambiguous bar, gap open exits, and entry day exits were retained; compounded cash return reconciliation passed. The system still rejects trading; 15min and +2R management are absent. All 12 sensitivity revised DD table in latest observation; no parameter retuning was done.

The notes are about a trading strategy for SOL. It uses a 15-minute bar-open point-check proxy. The strategy takes 25% of the profit at gross+2R and sets the remaining stop to the entry point, with a net 3R target. It checks for a half-ATR pullback within 3 days of the first prior 20-day high breakout. The stop is set to 2 prior-completed-day ATR20 with a 20-day timeout. The buy fee is 25bp credited in coin and the sell fee is 25bp in USD. There is a 10bps lip baseline and 30bps stress on each side. The training period from 2024-25 had a return of 10.5767%, a drawdown of 35.0750%, and a win rate of 57.14285714285714%. The holdout period from Jan 1 to Sep 11 2026 had a return of -4.6600%, a drawdown of 21.7915%, and a win rate of 50%. The stress was -5.4197%. The immediate breakout control had the same cost and exit as the holdout, with a return of 4.0652% and 6 trades. The strategy has three premises: different timeframes and policies, small samples do not establish a prospective edge, and missing observations are skipped. The vectorbt.from_orders cash is reconciled with explicit credited fee conversion. The SOL history starts from Aug 2024 and has a shortened training period. The source of the 15-minute open is a trade bar proxy, not historical executable quotes or actual venue fills. The conclusion is that this is a research comparator and not an automatic entry strategy. All 192 cells are retained in the observations. The sources are 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 the SOL cryptocurrency. It uses a 15-minute bar-open point-check proxy, which is a way to estimate the opening price of a trade. The strategy takes 25% of the profit at a certain point, called gross+2R, and sets a stop loss at the entry point for the remaining amount, with a target of net 3R. This means it aims to make three times the risk amount. The strategy checks for a half Average True Range (ATR) pullback, which is a measure of price volatility, within three days of a breakout from a 20-day high. ATR is a way to measure how much prices move. The stop loss is set at two times the prior completed day's ATR, with a 20-day timeout. There are fees for buying and selling, 25 basis points in coin and 25 basis points in USD, with a 10 basis point slip baseline and 30 basis point stress on each side. Basis points are a way to measure small percentages. The strategy was trained on data from 2024-25 and had a return of 27.9720%, a drawdown of 19.8678%, and a win rate of 75%. Drawdown is the maximum loss from a peak to a trough. The holdout period from January 1 to September 11, 2026, had a return of -12.1890%, a drawdown of 18.3921%, and a win rate of 33.3333%. The stress test result was -13.0386%. The immediate breakout control had the same cost as the exit holdout, with a result of -23.7294% and seven trades. The strategy is based on two premises: that different timeframes and policies cannot be substituted for older models, and that small samples and known hypothesis discovery do not establish a prospective edge. Edge refers to the ability to make profitable trades. The third premise is that missing observations are skipped or entries are cancelled, not treated as zero returns. The vectorbt library is used to reconcile cash from orders, and the end open positions are marked without invented close prices. The SOL history starts in August 2024, with shortened training. The source of the 15-minute open data is a trade bar proxy, not historical executable quotes or actual venue fills, which means it's an estimate of the opening price. Gaps in the data can delay exits. The conclusion is that this is a research comparator, not an automatic entry strategy, and all 192 cells are retained in the observations. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

The notes from the automated trading fund manager are as follows. The manager uses a 15-minute bar-open point-check proxy, which is a way to estimate the opening price of a trade. The manager takes 25% of the profit at a gross return of 2R, which is a measure of the profit relative to the risk, and sets the remaining stop to the entry point, with a net target of 3R. The manager checks for certain conditions before making a new entry. The strategy involves a half-ATR pullback, which is a price movement that is half the average true range, a measure of price volatility, within 3 days of a prior 20-day high breakout. The stop loss is set at 2 times the prior completed day's average true range, with a 20-day timeout. The manager also considers buy and sell fees, with a 25 basis point fee credited in coin and a 25 basis point fee in USD, and a 10 basis point slip baseline and 30 basis point stress on each side. The training period from 2024 to 2025 showed a return of 42.3123%, a drawdown of 37.5219%, and a win rate of 53.84615384615385%, with 13 closed trades and 1 open trade. The holdout period from January 1 to September 11, 2026, showed a return of 19.2067%, a drawdown of 14.5427%, and a win rate of 100%, with 3 closed trades and 1 open trade, and a stress test result of 17.5499%. The manager notes that the premise of the strategy is that different timeframes and policies differ, and that there is no substitution for the older frozen ETH model. The manager also notes that small samples and known 2026 hypothesis discovery do not establish a prospective edge, and that missing observations are skipped and entries are cancelled, not zero returns. The manager uses a vectorbt.from_orders cash reconciled with explicit credited fee conversion, and marks end open positions without invented close. The source of the 15-minute open is a trade bar proxy, not historical executable quotes or actual venue fills, and gaps can delay exits. The conclusion is that this is a research comparator, and not an automatic entry, and all 192 cells are retained in 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 news scan found the following notes from an automated trading fund manager. The manager uses a 15-minute bar-open point-check proxy, which is a way to estimate the opening price of a trade. The manager takes 25% of the profit at a gross return of 2R, which means twice the risk, and sets the remaining stop to the entry point, with a target of 3R. The manager checks for certain conditions before making a new entry. One condition is a half-ATR pullback, which means a price drop of half the average true range, within 3 days of a prior 20-day high breakout. The stop is set at 2 times the prior completed day's average true range, with a 20-day timeout. The manager also considers fees, with a buy fee of 25 basis points credited in coin and a sell fee of 25 basis points in USD. The manager uses a 10 basis point slip baseline and 30 basis point stress on each side. The training period from 2024 to 2025 had a return of -27.1269%, a drawdown of 37.0512%, and a win rate of 37.5%, with 8 closed trades and 1 open trade. The holdout period from January 1 to September 11, 2026 had a return of -0.2928%, a drawdown of 21.3949%, and a win rate of 66.66666666666666%, with 3 closed trades and 0 open trades, and a stress of -1.4822%. The manager notes that the immediate breakout control had the same cost as the exit holdout, with a return of -12.7035% and 5 trades. The manager makes three premises: that different timeframes and policies differ, that small samples and known 2026 hypothesis discovery do not establish a prospective edge, and that missing observations are skipped and entries are cancelled, not zero returns. The manager uses vectorbt to reconcile cash from orders and credits fees, and marks end open positions without invented close. The source of the 15-minute open is a trade bar proxy, not historical executable quotes or actual venue fills, and gaps can delay exits. The manager concludes that this is a research comparator and not an automatic entry, and all 192 cells are retained in observations. The sources are 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, referred to as C91204. It uses a 15-minute bar-open point-check proxy, which is a way to track the opening price of a trade. The strategy takes 25% of the profit at a certain point, called gross+2R, and sets a stop loss at the entry point, with a target of net 3R. It checks for certain conditions before entering a new trade. One condition is a half-ATR pullback, which is a price movement of half the average true range, within 3 days of a prior high point. The stop loss is set at 2 times the average true range of the prior 20 days, with a 20-day time limit. The strategy also takes into account fees, such as a 25 basis point buy fee credited in coin and a 25 basis point sell fee in USD. The training period from 2024 to 2025 had a return of -9.96%, a drawdown of 33.29%, and a win rate of 43.75%. The holdout period from January 1 to September 11, 2026 had a return of 1.19%, a drawdown of 10.12%, and a win rate of 66.67%. The strategy is based on certain premises, including that different time frames and policies should not be substituted for older models. The data source is a 15-minute open trade bar proxy, which may not reflect actual executable quotes or venue fills. The conclusion is that this strategy is for research comparison only and does not automatically enter trades.

The notes are from a frozen system, numbered 2535 and 2536. This system uses a 15-minute bar-open point check as a proxy. It takes 25% of the profit at a gross return of 2 times the risk, and sets the remaining stop to the entry point, with a target of 3 times the risk. The system checks for these conditions before making a new entry. It looks for a half Average True Range pullback within 3 days of the first breakout above the 20-day high. The stop is set at 2 times the prior completed day's Average True Range, with a 20-day timeout. The buy fee is 25 basis points, credited in coin, and the sell fee is 25 basis points, paid in USD. There is a 10 basis point slip baseline and 30 basis point stress on each side. The training period from 2024 to 2025 had a return of -14.7836%, a drawdown of 26.5712%, and a win rate of 20%, with 5 closed trades and 0 open trades. The holdout period from January 1 to September 11, 2026, had a return of 6.8464%, a drawdown of 8.7633%, and a win rate of 66.6667%, with 3 closed trades and 0 open trades, and a stress of 5.5719%. The immediate breakout control had the same cost as the exit holdout, with a return of -8.3834% and 6 trades. There are three premises: different timeframes and policies do not substitute for older models, small samples and known hypothesis discovery do not establish a prospective edge, and missing observations are skipped and entries are cancelled, not treated as zero returns. The vectorbt system reconciles cash from orders with explicit credited fee conversion, and marks end open positions without invented close prices. The source of the 15-minute open data is a trade bar proxy, not historical executable quotes or actual venue fills, and gaps can delay exits. This is a research comparator and not an automatic entry system, with all 192 cells retained in observations. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.