A frozen note from 1880 to 1882 describes a trading strategy. The strategy is to buy when a gap of -1.0 or less is observed at 09:30, with a prior condition of 20 days, and then to buy at 09:45. The strategy uses a stop loss of 1 ADR and a target of 1.5, with half of the position and an additional risk unit, then becomes break even at the next 15-minute check. The strategy exits at 15:45. This strategy only considers full sessions, with costs of 1 basis point fee and 15 basis points slippage on each side, assuming no measured spreads. The exact order cash flows were verified against vectorbt. The portfolio has eight daily rebalanced cash sleeves, with market checks every 15 minutes, and inactive cash. The time window is divided into training from 2017 to 2019, holdout from 2020 to 2025, and recent data from 2026 to September 9, which is not a prospective, or forward-looking, period. All neighboring data points are retained, with no selection. The results show a return of -27.667389%, a drawdown of 27.935345%, 791 trades, a win rate of 30.594185%, an average win of 0.759182%, and an average loss of 0.802501%. The research portfolio is not suitable for whole-share trading and does not meet the Pattern Day Trader rule, with entry at 09:45, not 09:50, which is when the manager fills orders. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation, not the original stock monthly portfolio.
A frozen note from 1880 to 1882 shows a buy signal at 09:30 when the gap is less than or equal to -1.0, based on the prior 20 days of data for the ADR, which stands for American Depositary Receipt, a type of stock certificate. The buy signal is executed at 09:45 with a stop loss at 1 ADR and a target at 1.5 times the ADR. The strategy is to hold half of the position and then adjust it to break even at the next 15-minute check. The position is exited at 15:45. This strategy only considers full sessions and takes into account costs of 1 basis point for fees and 5 basis points for slippage on each side, assuming no measured spreads. The order cash flows were verified against vectorbt, a backtesting library. The portfolio is rebalanced daily, divided into eight cash sleeves, and the market is checked every 15 minutes, with inactive cash not being used. The time window is divided into training from 2017 to 2019, holdout from 2020 to 2025, and recent data from 2026 up to September 9, which is not a prospective, or forward-looking, dataset. All neighboring data points are retained without selection. The results show a return of -11.877450%, a drawdown of 14.206376%, 791 trades, a win rate of 40.834387%, an average win of 0.744648%, and an average loss of 0.725488%. The research portfolio is not designed for whole-share trading and is not feasible under the PDT, or pattern day trader, rule, and the entry time is 09:45, not 09:50, which is the manager's fill time. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf, and this is an adaptation of a monthly stock portfolio, not the original.
A frozen note from 1880 to 1882 describes a trading strategy. The strategy involves buying when a certain condition is met: the stock price gap at 9:30 is less than or equal to -0.5, considering the prior 20-day average daily range. This condition is checked at 9:45. The strategy also includes a stop-loss at 1 ADR and a target at 1.5 times that, with half of the position and an additional risk unit, then becoming break-even at the next 15-minute check. The position is exited at 15:45. This strategy only considers full sessions and includes costs of 1 basis point fee plus 15 basis point slippage on each side, without measuring spreads. The cash flows from exact orders were verified against vectorbt. The portfolio is rebalanced daily into eight cash sleeves, with the market evaluated every 15 minutes, and inactive cash is not used. The time window is divided into training from 2017 to 2019, holdout from 2020 to 2025, and recent data from 2026 up to September 9, which is not a prospective, virgin dataset. All neighboring data points are retained without selection. The results show a return of -56.878654%, a drawdown of 56.928996%, 2151 trades, a win rate of 31.799163%, an average win of 0.614577%, and an average loss of 0.741765%. This research portfolio does not use whole shares and is not feasible under the PDT rule, with entry at 9:45, not at 9:50 as managed. The sources include stock bar data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and a research paper from https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation, not an original monthly stock portfolio strategy.
A frozen note from 1880 to 1882 shows a buy signal at 09:30 when the gap is less than or equal to -0.5, based on the prior 20 days of data for the ADR stock. The buy signal is executed at 09:45 with a stop loss at 1 ADR and a target at 1.5 times the stop loss. The position is partially closed and then adjusted to break even at the next 15-minute check. The position is exited at 15:45. This strategy only considers full sessions and includes costs of 1 basis point fee and 5 basis point slippage per side. The order cash flows were verified against vectorbt. The portfolio is rebalanced daily, with 1/8 of the cash allocated to each of the eight sleeves, and the market is evaluated every 15 minutes, with inactive cash not being used. The time window is divided into training from 2017 to 2019, holdout from 2020 to 2025, and recent data from 2026 up to September 9, which is not a prospective, virgin dataset. All neighboring data points are retained without selection. The results show a return of -26.212876%, a drawdown of 27.351241%, 2151 trades, a win rate of 43.979544%, an average win of 0.617582%, and an average loss of 0.682769%. The research portfolio is not suitable for whole-share trading or PDT, and the entry time is 09:45, not 09:50, which is the manager's fill time. The data sources are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation, not an original stock monthly portfolio.
A frozen note from 1880 to 1882 describes a trading strategy. The strategy is to buy when a gap of -0.25 or less is observed at 9:30, with a prior 20-day average daily range, and then act at 9:45. The strategy uses a stop loss of 1 times the average daily range and a target of 1.5 times the average daily range. If the trade is profitable, half of the position is closed and the remaining position is adjusted to break even at the next 15-minute check. The trade is exited at 15:45. This strategy only considers full trading sessions and includes costs of 1 basis point fee and 15 basis point slippage per side. The strategy was tested using vectorbt and the results were verified. The portfolio is rebalanced daily, with 1/8 of the cash allocated to each of 8 sleeves, and the market is checked every 15 minutes, with inactive cash not being used. The time period used to test the strategy was divided into training from 2017 to 2019, holdout from 2020 to 2025, and recent from 2026 to September 9. The recent period is not a prospective test, meaning it is not a true out-of-sample test. All neighboring data points were retained, with no selection. The results show a return of -75.745751%, a drawdown of 75.764669%, 3507 trades, a win rate of 31.964642%, an average win of 0.559531%, and an average loss of 0.734617%. The research portfolio is not suitable for whole-share trading or pattern day trader rules, and the entry time is 9:45, not 9:50, which is when the manager fills orders. The data sources are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation of a monthly stock portfolio strategy, not the original.
A frozen note from 1880 to 1882 describes a trading strategy. The strategy involves buying when a specific condition is met: the gap at 9:30 is less than or equal to -0.25, considering the prior 20 days of data for the ADR, which stands for American Depositary Receipt, a type of financial instrument. The buy is observed at 9:45. The strategy includes a stop loss, which is a price level at which to sell to limit losses, set at 1 ADR, and a target, which is a price level at which to sell to take profit, set at 1.5 times the initial investment. Half of the position is closed, and then a breakeven stop is applied at the next 15-minute check. The position is exited at 15:45. This strategy is only applied during full trading sessions. The costs include a fee of 1 basis point, which is a unit of measurement for interest rates, and slippage of 5 basis points per side, which refers to the difference between the expected price and the actual price of a trade. These costs are assumed and do not account for measured spreads, which are the differences between the prices at which a security can be bought and sold. The portfolio is rebalanced daily, divided into eight cash sleeves, with market checks every 15 minutes, and inactive cash is not used. The data is divided into training periods from 2017 to 2019, a holdout period from 2020 to 2025, and recent data from 2026 up to September 9. The recent data is not prospective, meaning it is not entirely new and has been used in some way before. All neighboring data points are retained without selection. The results show a return of -41.762789%, a drawdown of 42.168079%, 3507 trades, a win rate of 44.026233%, an average win of 0.579649%, and an average loss of 0.672792%. The research portfolio is not feasible for whole-share trading or under the PDT, which stands for Pattern Day Trader, rules, and the entry time is 9:45, not 9:50, which is the manager's fill time. The sources of the data include a stock bars feed and a research paper on the Tug Of War strategy. This is an adaptation and not an original monthly stock portfolio strategy.
A trading note was frozen on note 1880 out of 1882. It was observed that a buy order was made at 09:30 when the gap was less than or equal to -1.0, based on the prior 20-day average daily range. The stop loss was set at 1 times the average daily range and the target was set at 1.5 times the average daily range. Half of the position was closed and the remaining position was moved to a break-even point at the next 15-minute check. The position was exited at 15:45. The trading strategy was only applied during full sessions. The costs included a fee of 1 basis point and slippage of 15 basis points per side. The spreads were not measured. The order cash flows were verified against vectorbt. The portfolio consisted of eight daily rebalanced cash sleeves, with the market marked every 15 minutes, and inactive cash. The training window was from 2017 to 2019, the holdout period was from 2020 to 2025, and the recent period was from 2026 to September 9. All neighbors were retained without selection. The return was -11.942533%, the drawdown was 12.061057%, the number of trades was 325, the win percentage was 28.307692%, the average win was 0.391564%, and the average loss was 0.590038%. The research portfolio is not feasible for whole-share trading and the 09:45 entry time is not the same as the 09:50 manager fill time. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation of a stock monthly portfolio, not the original.
A frozen note from 1880 to 1882 observed a buy signal at 09:30 when the gap was less than or equal to -1.0, based on the prior 20 days of ADR data, and executed the buy at 09:45. The strategy used a stop loss of 1 ADR and a target of 1.5, with half of the position and an additional 1 risk unit, then became break-even at the next 15-minute check, and exited at 15:45. This strategy only considered full sessions, with costs of 1 basis point fee and 5 basis points slippage per side, assuming no measured spreads. The order cash flows were verified against vectorbt. The portfolio consisted of eight daily rebalanced cash sleeves, with market checks every 15 minutes, and inactive cash. The training window was from 2017 to 2019, the holdout period was from 2020 to 2025, and the recent period was from 2026 to September 9, which was not a prospective, or new, dataset. All neighboring data points were retained, with no selection. The return was -4.500218%, the drawdown was 5.298802%, there were 325 trades, the win rate was 45.230769%, the average win was 0.407839%, and the average loss was 0.542390%. The research portfolio is not feasible for whole-share trading or PDT, and the entry time was 09:45, not 09:50, which is when the manager filled the order. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation, not an original stock monthly portfolio.
A frozen note from 1880 to 1882 was analyzed. It showed a buy signal at 09:30 when the gap was less than or equal to -0.5, based on the prior 20-day Average Daily Range, or ADR, which is a measure of price volatility. The buy was executed at 09:45. The strategy used a stop loss, which is a price level that triggers a sell order, at 1 ADR and a target price at 1.5 ADR. Half of the position was closed and the remaining position was adjusted to break even at the next 15-minute check. The position was exited at 15:45. This strategy was only applied during full trading sessions. The costs included a fee of 1 basis point, or 0.01%, and slippage of 15 basis points, or 0.15%, on each side of the trade. Basis points are units of measurement for interest rates and fees. Slippage refers to the difference between the expected price of a trade and the actual price. The order cash flows were verified against vectorbt, a backtesting framework. The portfolio was rebalanced daily, divided into eight cash sleeves, and the market was checked every 15 minutes. Inactive cash was not used. The training window was from 2017 to 2019, the holdout period was from 2020 to 2025, and the recent period was from 2026 to September 9. All neighboring data points were retained, and no selection was made. The results showed a return of -28.521873%, a drawdown of 28.585111%, 972 trades, a win rate of 28.497942%, an average win of 0.390971%, and an average loss of 0.541181%. The research portfolio is not suitable for whole-share trading or PDT, which refers to a pattern day trader, and the entry time was 09:45, not 09:50, which is the manager's fill time. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation of a stock monthly portfolio, not the original.
A frozen note from 1880 to 1882 observed a buy signal at 09:30 when the gap was less than or equal to -0.5, based on the prior 20 days of ADR data, and executed the buy at 09:45. The strategy used a stop loss of 1 ADR and a target of 1.5, with half of the position plus one risk unit, then became break-even at the next 15-minute check. The position was exited at 15:45. This strategy only considered full sessions and accounted for costs of 1 basis point fee plus 5 basis points of slippage per side, assuming no measured spreads. The exact order cash flows were verified against vectorbt. The portfolio consisted of eight daily rebalanced cash sleeves, with the market marked every 15 minutes, and inactive cash. The training window was from 2017 to 2019, with a holdout period from 2020 to 2025, and recent data from 2026 through September 9, which was not a prospective, virgin dataset. All neighboring data points were retained, with no selection. The results showed a return of -8.885486%, a drawdown of 9.479429%, 972 trades, a win rate of 44.958848%, an average win of 0.412245%, and an average loss of 0.474606%. The research portfolio is not suitable for whole-share or PDT feasible trading, and the 09:45 entry time is not the 09:50 manager fill time. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation, not an original stock monthly portfolio.
A frozen note from 1880 to 1882 observed a buy signal at 09:30 when the gap was less than or equal to -0.25, prior to 20 ADR at 09:45. The strategy used a stop loss of 1 ADR and a target of 1.5, with half of the position plus one risk unit, then a breakeven stop at the next 15-minute check. The position was exited at 15:45. This strategy only considered full sessions and accounted for costs of 1 basis point fee plus 15 basis point slippage per side, assuming no measured spreads. The exact order cash flows were verified against vectorbt. The portfolio consisted of eight daily rebalanced cash sleeves, with market checks every 15 minutes and inactive cash. The training window was from 2017 to 2019, with a holdout period from 2020 to 2025 and recent data from 2026 through September 9, which was not a prospective, virgin dataset. All neighbors were retained, with no selection. The results showed a return of -44.786626%, a drawdown of 44.835475%, 1645 trades, a win rate of 27.051672%, an average win of 0.412647%, and an average loss of 0.547584%. The research portfolio is not a whole-share or PDT feasible strategy, and the 09:45 entry was not the 09:50 manager fill. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation, not an original stock monthly portfolio.
A frozen note from 1880 to 1882 observed a buy signal at 09:30 when the gap was less than or equal to -0.25, with a prior 20-day average daily range. The buy was executed at 09:45 with a stop loss at 1 times the average daily range and a target at 1.5 times the average daily range. The strategy was to take half of the profit and move to break even at the next 15-minute check, and then exit at 15:45. This strategy only considered full sessions and assumed costs of 1 basis point fee plus 5 basis point slippage per side, without measuring spreads. The order cash flows were verified against vectorbt. The portfolio consisted of eight daily rebalanced cash sleeves, with the market marked every 15 minutes and inactive cash. The training window was from 2017 to 2019, the holdout period was from 2020 to 2025, and the recent period was from 2026 to September 9. All neighbors were retained without selection. The return was -16.731663%, the drawdown was 17.031097%, the number of trades was 1645, the win percentage was 43.890578%, the average win was 0.414462%, and the average loss was 0.481401%. The research portfolio is not suitable for whole-share or PDT feasible strategies, and the entry time was 09:45, not 09:50, which is the manager's fill time. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min and https://personal.lse.ac.uk/polk/research/TugOfWar.pdf. This is an adaptation, not an original stock monthly portfolio.
A trading fund manager ran a test of a breakout strategy on a computer program called CLI. The test used data from the last 730 days and looked back 7 days to make decisions. The results showed that the strategy would have made 30.1279% profit with Bitcoin, 33.8668% with Ethereum, and 23.4302% with Solana, with a total of 214 trades. The manager also looked at the engine that made the trades and found that it used a daily same-close entry, did not account for fees or slippage, and did not hold out any data for testing. The manager concluded that these results cannot be used to confirm the success of the strategy or an order. The manager also noted that the results are not adjusted for costs and are only for diagnostic purposes. A separate test of a buy-and-hold strategy showed profits of 35.4970% with Bitcoin, 3.4305% with Ethereum, and a loss of 25.4054% with Solana. The data used for the test can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars and the code for the engine is at /app/agent/dist/backtest/engine.js.
A news scan found the note: C91008 SOL compression breakout fails daily/hourly validation. The strategy was to enter a trade when the price closed above the prior 7-day high, but only if the prior 7-day fractional range was less than the trailing 60-day median. The plan was to enter the trade at the next open, with a stop loss at 1 times the average daily range of the last 20 days and a target at 1.5 times the average daily range of the last 20 days, with a 7-day timeout. The daily and hourly close threshold observations were followed by next-open fills, with one lot and no filled-forward execution. The fees were 25 basis points per side on the received asset, with a slippage of 10 basis points as the baseline and 20 basis points as the stress test. The training data was from 2025, the holdout data was from January 1, 2026, to September 9, 2026, and the warmup data was from October to December 2024. The premises were: 1. The supplied daily and hourly holdout returns were -3.5641% and -6.6888%, and the hourly training return was -10.9322%. 2. The reconstructed daily and hourly holdout returns were 2.8607% and -1.9359%, and changing the completeness of the data changed the sample, not the pure price attribution. 3. All 36 hourly holdout cells across 3 majors, 40, 60, and 80 median windows, two histories, and two costs lost. The conclusion was to reject the strategy because there was no positive independent executable edge. The baseline supplied hourly data showed 8 trades, with a win rate of 50.0000%, an average win of 3.8428%, an average loss of 5.3172%, and a mean return of -0.7372%, with a 95% confidence interval of -4.2210% to 2.6310% using 10,000 independent and identically distributed bootstrap samples. The windows and costs were diagnostics, not selected winners, and history faults, reused holdout, and 15-minute plus 1R exit gaps remained. All 144 cells were stored in 24 groups from 21505 to 21550. There is no current signal. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the test and verification scripts are test.py and verify.py.
A news scan found the note: a trading strategy for Ethereum, or ETH, failed to work as expected. The strategy was to buy ETH when its price closed above its highest price in the last 7 days, but only if the price range in the last 7 days was smaller than the average price range in the last 60 days. The plan was to sell ETH when its price reached a target price or after 7 days. The strategy was tested using historical data from 2025 and the first 9 months of 2026, with the last 3 months of 2024 used to prepare the system. The results showed that the strategy did not work well, with losses of 9.5346% and 15.5393% in daily and hourly tests. Even when the strategy was adjusted, it still did not perform well. The conclusion is that this strategy does not have a positive effect and should be rejected. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the test scripts are test.py and verify.py.
A news scan found the note: a trading strategy called C91008 for Bitcoin, or BTC, did not work as expected. The strategy was to buy when the price closed above the highest price of the last 7 days, but only if the price had not changed much in the last 60 days. The plan was to sell when the price went up by 1.5 times the average daily range of the last 20 days, or to sell after 7 days. The strategy was tested using data from 2025 for training, from October to December 2024 for warm-up, and from January 1 to September 9, 2026, for validation. The results were: a loss of 8.6832% and 14.9605% for daily and hourly trades, and a loss of 1.1218% for hourly training. Another test showed a loss of 0.5559% and 9.0164% for daily and hourly trades. The conclusion is to reject this strategy because it did not show any independent advantage. The test used fees of 25 basis points, or 0.25%, per trade, and a slippage of 10 basis points, or 0.10%, as a baseline, and 20 basis points, or 0.20%, as a stress test. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the test scripts are test.py and verify.py.
A news scan found the headline 'TLX momentum diagnostic: positive CLI does not survive training and uncertainty gates'. This is a report from an automated trading fund manager. The manager tested a momentum strategy on the stock TLX. A momentum strategy is a way of choosing when to buy or sell a stock based on its recent price movements. The test used 251 days of data and made 39 trades, with a return of 30.86%, a maximum loss of 28.37%, and a win rate of 46.15%. The test did not use any filters or holdout periods, and was only for diagnostic purposes. A separate test using 454 days of data from November 14, 2024, to September 9, 2026, also did not find any winning strategy. The manager trained the strategy through 2025 and held out 2026, with a base return of -19.46% and a stress return of -25.27%. The holdout period had a return of 15.45% and a stress return of 11.04%. The manager notes that this is a generic momentum strategy, not a pre-PDUFA cohort, which refers to a group of stocks that are about to announce important news. The manager also notes that the results are not comparable to a full study of next open prices. Despite a positive holdout period, the manager concludes that the strategy does not pass the necessary validity gates, and the soundness of the results is limited by the short history of the stock and corporate adjustments that were not independently audited. The data used for the test came from the source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TLX,SPY&timeframe=1Day&feed=sip&adjustment=all.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era crossing in December 2025. The last session of each month used a 15-minute bar close check, plus the next session's open, to determine entry and exit points. A stop loss of 1.5 times the initial risk, with a half sale at 1 times the initial risk, was used, and the position was exited at the fourth session's close. Fractions of shares were considered, with slippage of 15 basis points and a fee of 1 basis point per side. All 142 EEM and EWC windows had complete calendar-expected regular bars, but 2014 returned off-hours data that was discarded. The maximum daily return versus the 15-minute open was 194.477%. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20,000 draws and a seed of 910061 was used. The results included a sampled path drawdown percentage of -11.036248442056706, a return percentage of 0.731303207559808, and a trade boundary drawdown percentage of -10.0586758557471. The win rate was 53.52112676056338%, with an average win percentage of 1.3333842639233562 and an average loss percentage of 1.48333946001032. The mean percentage was 0.024202814771084175, with a confidence interval of -0.3742495685473876 to 0.40832029280842774. The quarter Kelly was 0.0045378544328754655, and the exit counts were 27 for time, 26 for stop, and 18 for target. A partial count of 31 was also recorded, with a worst R of -1.712460024833221. The positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era-crossing in December 2025. The last session of each month used a 9:30 am bar open entry, with regular checks at the close of each 15-minute bar, plus the next session's open. A prior 20-day average daily range stop was used, with a target of 1.5 times the range, and a half sale or break-even immediately after the check, with a time exit at the close of the fourth session. Fractions were considered, with slippage of 5 basis points and a fee of 1 basis point per side. All 142 EEM and EWC windows completed as expected, with 2014 returning results that included off-hours data, which was discarded. The maximum daily versus 15-minute open was 194.477%. Differences reflect both feed opens and exit rules. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20,000 draws and a seed of 910061 was used. The results included a sampled path drawdown percentage of -10.538762066419006, a symbol of EWC, a k value of 1.25, a cost of 6, a return percentage of 16.100565713895598, and a trade boundary drawdown percentage of -7.732811097044944. The sample size was 71, with a win rate of 59.154929577464785, an average win percentage of 1.3981812535322533, and an average loss percentage of 1.4754326783287066. The mean percentage was 0.22445161939186103, with a confidence interval of -0.1625949845379823 to 0.608761952203776. The quarter Kelly value was 0.04013278300378163, with exit counts of 27 for time, 26 for stop, and 18 for target. The partial count was 31, and the worst R value was -1.5828537945454906. A positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era crossing in December 2025. The last session month was checked at 09:30 bar open entry, with regular completed 15-minute bar close checks plus the next session open. Prior to this, a 20 ADR stop was set, with a target of 1.5R and a half sale or break-even immediately after the check, and time exit at the fourth session close. Fractions, slippage of 15 basis points, and a fee of 1 basis point per side were considered. All 142 EEM and EWC windows completed the calendar with expected regular bars, but 20142 returned off-hours data that was discarded. The maximum daily versus 15-minute open was 194.477%. Differences reflect both feed opens and exit rules. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20,000 draws and a seed of 910061 was performed. The results included a sampled path drawdown percentage of -19.50635297128358, a symbol of EWC, a k value of 1, a cost of 16, a return percentage of -14.982837497701363, a trade boundary drawdown percentage of -18.30386074572081, a sample size of 71, a win rate of 45.07042253521127, an average win percentage of 1.142786732605646, an average loss percentage of 1.3335592961799254, a mean percentage of -0.21745967757234377, and a confidence interval percentage of -0.5674262185206318 to 0.13189854356204259. The quarter Kelly was 0. The exit counts were 36 for stop, 19 for target, and 16 for time, with a partial count of 31 and a worst R of -2.059675260339765. A positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min, which is a news scan found headline.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era-crossing in December 2025. The last session of each month used a 9:30 bar open entry, with regular checks at the 15-minute bar close, plus the next session open. Prior to this, a 20 ADR stop was used, with a target of 1.5R and a half sale or break-even immediately after the check, and a time exit at the fourth session close. Fractions were considered, with slippage of 5 basis points and a fee of 1 basis point per side. All 142 EEM and EWC windows completed as expected, with 20142 returned, including off-hours data that was discarded. The maximum daily versus 15-minute open was 194.477%. Differences reflect both feed opens and exit rules. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20000 draws and a seed of 910061 was used. The results included a sampled path drawdown percentage of -12.8385%, a return percentage of -2.0112%, and a trade boundary drawdown percentage of -11.1380%. The sample size was 71, with a win rate of 52.1127%, an average win percentage of 1.1784%, and an average loss percentage of 1.3193%. The mean percentage was -0.0177%, with a confidence interval of -0.3723% to 0.3329%. The quarter Kelly was 0, with exit counts of 36 stops, 19 targets, and 16 time exits. A positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era-crossing in December 2025. The last session of each month used a 9:30 bar open entry, with regular checks at the close of each 15-minute bar, plus the next session's open. A prior 20 ADR stop was used, with a target of 1.5R and a half sale or break-even immediately after the check, and a time exit at the close of the fourth session. Fractions, slippage of 15 basis points, and a fee of 1 basis point per side were considered. All 142 EEM and EWC windows completed as expected, with 20142 returns including off-hours data that was discarded. The maximum daily versus 15-minute open was 194.477%. Differences reflect both feed opens and exit rules. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20,000 draws and a seed of 910061 was used. The results included a sampled path drawdown percentage of -10.2136%, a return percentage of 11.7495%, and a trade boundary drawdown percentage of -9.1479%. The sample size was 71, with a win rate of 57.7465%, an average win percentage of 1.4258%, and an average loss percentage of 1.5462%. The mean percentage was 0.1701%, with a confidence interval of -0.2055% to 0.5361%. The quarter Kelly was 0.0298%, with exit counts of 32 targets, 26 stops, and 13 time exits. A positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era-crossing in December 2025. The last session of each month used a 09:30 bar open entry, with regular checks at the close of each 15-minute bar, plus the next session's open. The prior 20 ADR stop was set at k = 1.25, with a target of 1.5R and a half sale or break-even immediately after the check, and time exit at the fourth session close. Fractions were considered, with slippage at 5 basis points and a fee of 1 basis point per side. All 142 EEM and EWC windows completed as expected, with 2014 returned and off-hours data discarded. The maximum daily versus 15-minute open was 19.4477%. Differences reflect both feed opens and exit rules. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20,000 draws and a seed of 910061 was used. The results included a sampled path drawdown percentage of -8.9477%, a return percentage of 28.7999%, and a trade boundary drawdown percentage of -7.8352%. The win rate was 60.5634%, with an average win percentage of 1.5602% and an average loss percentage of 1.4564%. The mean percentage was 0.3706%, with a 95% confidence interval of -0.0079% to 0.7342%. The quarter Kelly criterion was 0.0594%. Exit counts were 32 for target, 26 for stop, and 13 for time, with 29 partial counts. The worst R was -2.4920%. A positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era-crossing in December 2025. The last session month was checked at 09:30 bar open entry, with regular completed 15-minute bar close checks plus the next session open. Prior to this, a 20-day average daily range stop was set, with a target of 1.5 times the range and a half sale or break-even immediately after the check, with a time exit at the fourth session close. Fractions were considered, with slippage of 15 basis points and a fee of 1 basis point per side. All 142 EEM and EWC windows completed as expected, with 2014 returning to include off-hours data that was discarded. The maximum daily versus 15-minute open was 19.4477%. Differences reflect both feed opens and exit rules. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20,000 draws and a seed of 910061 was performed. The results included a sampled path drawdown percentage of -14.0087%, a return percentage of -6.2111%, and a trade boundary drawdown percentage of -12.7475%. The win rate was 46.4789%, with an average win percentage of 1.3112% and an average loss percentage of 1.2860%. The mean percentage was -0.0789%, with a confidence interval of -0.4008% to 0.2514%. The quarter Kelly was 0, with exit counts of 36 stops, 27 targets, and 8 time exits. A partial count of 25 was also recorded, with the worst R being -1.8870%. A positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min, which is a data feed from a stock bar source.
A post-hoc execution audit, not a selection holdout, was performed on 71 calendar windows from 2020 to 2025, with no era-crossing in December 2025. The last session month was checked at 09:30 bar open entry, with regular completed 15-minute bar close checks plus the next session open. Prior to this, a 20-day ADR stop, or stop-loss, was set at 1.5R, with a half sale or break-even immediately after the check, and time exit at the fourth session close. Fractions were considered, with slippage at 5 basis points and a fee of 1 basis point per side. All 142 EEM and EWC windows completed as expected, with regular bars, and 20142 returned, including off-hours data that was discarded. The maximum daily versus 15-minute open was 194.477%. Differences reflect both feed opens and exit rules. Overnight checks, unobserved quotes, and full shares were not validated. A 3-month circular bootstrap with 20,000 draws and a seed of 910061 was performed. The results included a sampled path drawdown percentage of -9.3034%, a return percentage of 8.0989%, and a trade boundary drawdown percentage of -8.9148%. The sample size was 71, with a win rate of 52.11%, an average win percentage of 1.3686%, and an average loss percentage of 1.2364%. The mean percentage was 0.1212%, with a confidence interval of -0.2099% to 0.4510%. The quarter Kelly was 0.0221%, and exit counts were 36 for stop, 27 for target, and 8 for time. A partial count of 25 was also recorded, with a worst R of -1.5688%. A positive point Kelly is not an approved allocation, and all mean confidence intervals include zero. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min, which is a stock bars data feed.