This is a description of a trading control for the cryptocurrency XRP. It uses a fixed Donchian strategy, which is a type of trading strategy that uses the highest and lowest prices of a security over a certain period of time, in this case, the prior 20 calendar-day high for entry and prior 10-day low for exit at the next exact open, with a time frame of 480 and 240 hours. The data used is incomplete, with missing calendar hours and prices before September 13, 2022, at 02:00 UTC, and the incomplete September 13 day is excluded. The Vectorbt trading system is used, which credits a coin buy fee equivalent to 0.0025 and 0.9975, and a sell fee of 25 basis points, with a slip of 10 and 30 basis points for stress testing. The training period from 2024 to 2025 had a return of 110.893498%, a drawdown of 56.069200%, with 10 closed trades and a win rate of 40.000%, and no open trades. The reset period in 2026 had a return of 2.433934%, a drawdown of 28.004057%, with 4 closed trades and a win rate of 25.000%, and no open trades, with a stress return of 0.808026%. All 15, 20, and 30-day sensitivities were recorded, but no best parameter was selected. The calendar holdout is not newly untouched data. The return includes the open marked position, and the win rate is closed only, with no assumed final fill. This control does not require a 2 ATR stop, 3 R target, quarter at 2 R, or 15-minute exit management, so it is not a live policy validation. There was no fresh high entry across 7, and no late entry. The source of the data is from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment had 156 cells and 26910 to 27075 observations, with the current top 10 survivor selection limits generalization.
The C91302 XRP daily costed HIGH20 LOW10 control uses a fixed Donchian strategy, which is a type of trading strategy that uses prior highs and lows to make decisions, with a prior 20 calendar-day high as the entry point and a prior 10-day low as the exit point at the next exact open. This strategy requires 24 hours of OHLC, or open, high, low, and close, data per day, and only considers prices before September 13, 2022, at 02:00 UTC, excluding the incomplete data from September 13. The Vectorbt software is used to simulate the trading, taking into account a coin buy fee equivalent to 0.0025/0.9975 and a sell fee of 25 basis points, with a 10 basis point and 30 basis point stress test. The training period from 2024 to 2025 resulted in a return of 194.412594%, a drawdown of 49.868094%, with 6 closed trades and a win rate of 33.333%, and no open trades. The reset period in 2026, using known and reused holdout data, resulted in a return of 0.569605%, a drawdown of 27.161711%, with 3 closed trades and a win rate of 0.000%, and 1 open trade, with a stress return of -0.828169%. The strategy recorded sensitivities for 15, 20, and 30-day periods, but did not select a best parameter. The calendar holdout data is not newly untouched data. The return includes the open marked position, and the win rate is only calculated for closed trades, with no assumed final fill. This control does not require a 2 ATR stop, 3R target, quarter at 2R, or 15-minute exit management, so it is not a live policy validation. There were no fresh high entries across 7 periods, and no late entries. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment involved 156 cells and 26910-27075 observations, with the current top 10 survivor selection limiting generalization.
This is a description of a trading strategy for Litecoin, referred to as C91302 LTC hourly costed HIGH20 LOW10 control. The strategy uses a fixed Donchian system, which is a type of trading system that uses prior highs and lows to make decisions, with a 20-day high for entry and a 10-day low for exit at the next open, with a time frame of 480 and 240 hours. The data used only includes prices before September 13, 2022, at 02:00 UTC, and excludes the incomplete data from September 13. The Vectorbt system is used to simulate the trades, taking into account a buy fee equivalent to 0.25% and a sell fee of 25 basis points, with a slip of 10 basis points and a stress test of 30 basis points. The training period from 2024 to 2025 showed a return of 22.038201%, a maximum drawdown of 45.931078%, with 10 closed trades and a win rate of 40.000%. The reset period in 2026 showed a return of 3.611121%, a maximum drawdown of 12.675287%, with 2 closed trades and a win rate of 0.000%. The stress test return was 2.580579%. All 15, 20, and 30-day sensitivities were recorded, but no best parameter was selected. The calendar holdout data is not newly untouched. The return includes the open marked position, and the win rate is only for closed trades, with no assumed final fill. This control does not require a 2 ATR stop, 3R target, quarter at 2R, or 15-minute exit management, so it is not a live policy validation. There were no fresh high entries across 7, and no late entries. The data source is from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment includes 156 cells with observations from 26910 to 27075, and the current top 10 survivor selection limits generalization.
This is a description of a trading strategy for Litecoin (LTC). The strategy uses a fixed Donchian system, which is a type of trading system that uses prior highs and lows to make decisions, with a 20-day high entry point and a 10-day low exit point at the next open. The system requires 24 hours of data per day and only uses prices before September 13, 2022, 02:00 UTC, excluding the incomplete data from September 13. The system also takes into account a buy fee equivalent to 0.25% and a sell fee of 25 basis points, with a 10 basis point and 30 basis point stress test. The system was trained on data from 2024 to 2025 and had a return of -22.367606%, a maximum drawdown of 57.771811%, and a win rate of 33.333% for closed trades. The system was then reset and tested on known data from 2026, with a return of -4.170822%, a maximum drawdown of 16.265833%, and a win rate of 0% for closed trades. The stress test return was -5.123963%. The system recorded sensitivities for 15, 20, and 30-day periods, but did not select a best parameter. The data used for testing is not newly untouched data. The return includes the value of any open positions, and the win rate only includes closed trades. This strategy does not use a 2 ATR stop, 3R target, or quarter at 2R, and does not have a 15-minute exit management policy, so it is not a live policy validation. The system did not have any late entries. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment had 156 cells and 26910 to 27075 observations, and the current top 10 survivor selection limits generalization.
This is a description of a trading control for the SOL cryptocurrency. The control uses a fixed Donchian strategy, which is a type of trading strategy that uses prior highs and lows to make decisions, specifically the high from the prior 20 calendar days to enter a trade and the low from the prior 10 days to exit a trade, with the trade happening at the next exact open, and the control is checked every 480 or 240 hours. The data used only includes prices before September 13, 02:00 UTC, and excludes the incomplete data from September 13. The control also takes into account fees, with a buy fee equivalent to 0.0025 and a sell fee of 25 basis points, and slip fees of 10 and 30 basis points. The control was trained on data from 2024 to 2025 and had a return of 9.329324%, a maximum drawdown of 36.797732%, and a win rate of 50.000% for closed trades. When applied to known data from 2026, the control had a return of 1.972209%, a maximum drawdown of 24.527633%, and a win rate of 0.000% for closed trades. The control also recorded sensitivities for 15, 20, and 30-day periods, but did not select a best parameter. The return includes the value of any open positions, and the win rate only includes closed trades. The control does not require a 2 ATR stop, 3R target, quarter at 2R, or 15-minute exit management, which are types of risk management strategies, so it is not a live policy validation. The data for this control comes from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment used 156 cells and 26910 to 27075 observations, and the current top 10 survivor selection limits the generalization of the results. A news scan found the source of this data.
This is a description of a trading strategy for the SOL cryptocurrency. The strategy uses a fixed Donchian system, which is a type of trading system that uses high and low prices to make decisions, with a 20-day high entry point and a 10-day low exit point at the next opening price. The system requires 24 hours of price data per day and only uses prices before September 13, 2022, 02:00 UTC, excluding the incomplete data from September 13. The Vectorbt system is used to simulate the trading, taking into account a buy fee equivalent to 0.25% and a sell fee of 25 basis points, with a 10 basis point and 30 basis point stress test. The results of the training from 2024 to 2025 show a return of -10.124640%, a maximum drawdown of 42.460968%, 7 closed trades, and a win rate of 42.857%. The results of the reset from 2026 show a return of 3.896506%, a maximum drawdown of 17.407810%, 3 closed trades, and a win rate of 0.000%. The stress test return is 2.452493%. The system recorded sensitivities for 15, 20, and 30-day periods, but did not select the best parameter. The calendar holdout is not newly untouched data. The return includes the open marked position, and the win rate is only for closed trades, with no assumed final fill. This control does not require a 2 ATR stop, 3 R target, quarter at 2 R, or 15-minute exit management, so it is not a live policy validation. There were no fresh high entries across 7 periods, and no late entries. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment had 156 cells and 26910 to 27075 observations, with the current top 10 survivor selection limiting generalization. A news scan found the headline is not available, this is a raw script output.
This is a description of an automated trading strategy for Ethereum (ETH). The strategy uses a Donchian channel, which is a type of technical indicator, to determine when to buy and sell. It looks at the highest price in the last 20 days and the lowest price in the last 10 days to decide when to enter and exit trades. The strategy only considers complete calendar days and ignores any incomplete days, such as September 13. It also takes into account fees and slippage, which is the difference between the expected and actual prices of a trade. The strategy was tested using data from 2024 to 2025 and resulted in a return of 16.482836%, with a maximum drawdown of 41.890183%. It was also tested on a holdout set of data from 2026 and resulted in a return of -0.011938%, with a maximum drawdown of 22.646338%. The strategy does not use certain risk management techniques, such as a 2 ATR stop or a 3R target. The data used to test the strategy came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment involved 156 cells and 26910 to 27075 observations, and the top 10 surviving strategies were selected.
The C91302 ETH daily control uses a fixed Donchian strategy, which is a type of trading strategy that uses prior highs and lows to make decisions, with a 20-day high entry and a 10-day low exit at the next open. This strategy requires 24 hours of OHLC, or open, high, low, and close, data per day. Prices before September 13, 02:00 UTC are used, but the incomplete data from September 13 is excluded. The Vectorbt system is used to credit a coin buy fee equivalent to 0.0025 and 0.9975, and a sell fee of 25 basis points, with a 10 basis point and 30 basis point stress slip. From 2024 to 2025, the training return was -24.669800%, with a drawdown of 50.548573%, 12 closed trades, and a win rate of 41.667%. In 2026, the known and reused holdout return was -4.530094%, with a drawdown of 17.452215%, 3 closed trades, and a win rate of 0.000%. The stress return was -5.856989%. All 15, 20, and 30-day sensitivities were recorded, but no best parameter was selected. The calendar holdout is not newly untouched data. The return includes the open marked position, and the win rate is closed only, with no assumed final fill. This control does not require a 2 ATR stop, 3R target, quarter at 2R, or 15-minute exit management, so it is not a live policy validation. There was no fresh high entry across 7 days, and no late entry. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. This experiment has 156 cells and 26910 to 27075 observations, with a current top 10 survivor selection that limits generalization.
This is a description of a trading strategy for Bitcoin. The strategy uses a Donchian channel, which is a type of technical analysis, to determine when to enter and exit trades. It looks at the highest price in the last 20 days and the lowest price in the last 10 days. If the price reaches a new high, it enters a trade at the next opening price, and if the price reaches a new low, it exits the trade. The strategy only considers prices from before September 13, 2022, at 02:00 UTC, and excludes incomplete data from that day. The Vectorbt backtesting tool was used to simulate the strategy, taking into account fees of 0.25% for buying and 25 basis points for selling, with a 10 basis point to 30 basis point stress test. The results from 2024 to 2025 showed a return of 30.438863% and a maximum drawdown of 34.235134%. In 2026, the return was 18.078296% and the maximum drawdown was 12.151994%. The strategy was tested on 15, 20, and 30-day sensitivities, but no best parameter was selected. The data used for testing is not entirely new and untouched. The return includes the value of any open positions, and the win rate only considers closed trades. This strategy does not require a stop-loss or target profit, and it does not use a specific exit management system. The data for this experiment came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment involved 156 cells and 26910 to 27075 observations, and the current top 10 survivor selection may limit the generalization of the results. A news scan found the headline is not applicable here, this is a raw script output.
This is a description of a trading control for Bitcoin, denoted as C91302. The control uses a fixed Donchian strategy, which is a type of trading strategy that uses prior highs and lows to determine entry and exit points, with a prior 20 calendar-day high for entry and a prior 10-day low for exit at the next exact open. The control requires 24 hours of OHLC, or open, high, low, and close, data per day, and only considers prices before September 13, 2022, at 02:00 UTC, excluding the incomplete data from September 13. The control also accounts for a coin buy fee equivalent to 0.0025 and a sell fee of 25 basis points, with a slip of 10 basis points and a stress of 30 basis points. The training period from 2024 to 2025 resulted in a return of 32.159105%, a drawdown of 32.756294%, with 10 closed trades and a win rate of 30.000%. The reset period in 2026, using known and reused holdout data, resulted in a return of 18.337791%, a drawdown of 10.253074%, with 2 closed trades and a win rate of 50.000%. The stress return was 17.160774%. All 15, 20, and 30-day sensitivities were recorded, but no best parameter was selected. The calendar holdout is not newly untouched data. The return includes the open marked position, and the win rate is only for closed trades, with no assumed final fill. This control does not require a 2 ATR stop, 3R target, quarter at 2R, or 15-minute exit management, so it is not a live policy validation. There was no fresh high entry across 7 days, and no late entry. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The experiment consisted of 156 cells with 26910 to 27075 observations, and the current top 10 survivor selection limits generalization.
A trading system called Vectorbt was tested on the SOL cryptocurrency. The system buys when the closing price exceeds the highest closing price of the prior 20 calendar days, and sells when this condition is no longer true, using the exact opening price. The system was trained on data from August 26, 2024, to December 31, 2024, and tested on data from January 1, 2025, to September 12, 2026. The system charges a fee of 0.25% on each trade, and also accounts for slippage, which is the difference between the expected and actual price of a trade. The system uses daily high, low, open, and close prices from 24 complete hours of data, without filling in any missing data. The baseline training return was -72.51%, with a maximum drawdown of 73.42%, 133 trades, and a win rate of 15.79%. The holdout return was -32.09%, with a maximum drawdown of 32.42%, 55 trades, and a win rate of 18.18%. The system also tested a high threshold control, which had a return of -13.64% on 24 trades. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/crypto-bars and the source of the fee information to be https://docs.alpaca.markets/us/docs/crypto-fees. The system was not adopted due to various reasons, including disagreement between daily and hourly data, and missing required data. This system is not similar to other systems that use a 10-day low exit or a managed ETH pullback.
A trading strategy for SOL was tested using a system called Vectorbt. The strategy is to buy when the closing price is higher than the highest closing price of the last 20 calendar days, and sell when this is no longer true, using the exact opening price. The test used hourly data from 480 bars, trained from 2024 to 2025, with SOL starting on August 26, and a cash reset on January 1. The test also included a holdout period from 2026 through September 12. The strategy included fees of 25 basis points on each side, credited as an asset equivalent buy fee of 0.0025 and 0.9975, and slippage of 10 basis points and 30 basis points for stress testing. The daily high, low, open, and close prices were used from 24 complete hours, with missing calendar windows remaining unavailable and no filling. The baseline train return was 10.433149%, with a drawdown of 20.817749%, 24 trades, and a win rate of 45.833%. The holdout return was 18.625863%, with a drawdown of 14.948472%, 11 trades, and a win rate of 45.455%. The stress test return was 13.519467%. A high threshold control holdout return was 8.392065% with 7 trades. All 15, 20, and 30 day close and high price cells were recorded from 26572 to 83, with a known holdout range of 3.995 to 18.626%, but none were selected. The premise is that same-close gross gains are not evidence for a costed next-open rule. A counterexample is that daily SOL remains positive, but hourly disagreement and missing required 3R, partial, and 15-minute policy prevent adoption. This strategy is not a prior 10 low exit Donchian or managed ETH pullback. The conclusion is to reject this strategy as a live replacement. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A trading strategy for Ethereum (ETH) was tested using a system called Vectorbt. The strategy was to buy when the closing price exceeded the highest closing price of the prior 20 calendar days, and sell when this was no longer true, using the exact opening price. The test used hourly data from 480 bars, trained from 2024 to 2025, with the SOL period starting on August 26, and a cash reset on January 1. The test also included a holdout period from 2026 through September 12. The strategy included fees of 25 basis points on each side, credited to the asset equivalent buy fee of 0.0025/0.9975, and slippage of 10 basis points/30 basis points stress. The daily open, high, low, and close (OHLC) data was used from 24 complete hours, with missing calendar windows remaining unavailable and no filling. The baseline train return was -69.973193%, with a drawdown of 70.504557%, 197 trades, and a win rate of 17.766%. The holdout return was -20.782205%, with a drawdown of 25.913218%, 45 trades, and a win rate of 13.333%. The stress test result was -33.831787%. A high threshold control holdout resulted in -11.419497% with 21 trades. All 15/20/30 day close/high cost cells recorded 26572-83, with a known holdout range of -38.410 to -9.158%, but none were selected. The premise was that same-close gross gains are not evidence for a costed next-open rule. A counterexample showed that daily SOL remained positive, but hourly disagreement and missing required 3R/partial/15min policy prevented adoption. This short-hold toggle is not a prior 10 low exit Donchian or managed ETH pullback. The conclusion was to reject this strategy as a live replacement. The data sources were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the text describing the C91300 ETH daily costed close-screen control and HIGH sensitivity. This is about an automated trading system. Vectorbt is a tool used for backtesting, which means testing a trading strategy on past data. The system goes long, or buys, when the closing price exceeds the highest closing price of the prior 20 calendar days, and exits when this is no longer true, following the exact opening price. It uses hourly data from 480 bars. The system was trained on data from 2024 to 2025, with the SOL period starting on August 26, and the cash reset on January 1. The known holdout period is from 2026 through September 12. There is no claim that this is an untouched new out-of-sample, or OOS, test. Fees of 25 basis points, or 0.25%, are credited to the asset equivalent buy fee of 0.0025/0.9975, and slippage of 10 basis points/30 basis points is stressed. Daily open, high, low, and close, or OHLC, data is from 24 complete hours. Missing calendar windows remain unavailable, with no filling. The baseline train return is 5.185949%, the drawdown is 31.646448%, there are 41 trades, and the win rate is 29.268%. The holdout return is -21.109976%, the drawdown is 29.758847%, there are 13 trades, and the win rate is 15.385%, with an open rate of 1. The stress test result is -25.256777%. The HIGH threshold control holdout result is -16.281467% with 8 trades. All 15/20/30 day close and high cost cells recorded from 26572 to 83, with a known holdout range of -25.257% to -7.238%, were considered, but none were selected. The premise is that same-close gross cumulative log returns, or CLI, gains are not evidence for a costed next-open rule. A counterexample is that daily SOL remains positive, but hourly disagreement and missing required 3R/partial/15 minute policy prevent adoption. This short-hold toggle is not a prior 10 low exit Donchian or managed ETH pullback. The conclusion is to reject this as a live replacement. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about a trading strategy for Bitcoin, which is based on a set of rules. The strategy uses a concept called a'vector' to decide when to buy or sell Bitcoin. It goes long, meaning it buys, when the closing price exceeds the highest closing price of the prior 20 calendar days, and exits when this condition is no longer true, following the exact opening price of the next hour. This strategy is tested using 480 hours of data from 2024 to 2025, with the training period starting on August 26 and a cash reset on January 1. The results from 2026 are held out and not used to train the strategy, up to September 12. The strategy includes fees of 25 basis points, or 0.25%, on each side, credited to the asset equivalent buy fee of 0.0025 and 0.9975, and slippage of 10 basis points and 30 basis points for stress testing. The daily high, low, open, and close prices are used from 24 complete hours, with missing calendar windows remaining unavailable and not filled. The baseline train return is -78.978048%, the drawdown is 78.978048%, with 208 trades and a win rate of 14.423%. The holdout return is -25.349461%, the drawdown is 26.444395%, with 57 trades and a win rate of 12.281%, and an open rate of 0. The stress test return is -40.568953%. A high threshold control holdout return is -12.189031% with 28 trades. All 15, 20, and 30 day close and high cost cells recorded from 26572 to 83, with a known holdout range of -40.569% to -8.841%, were considered, but none were selected. The idea that same-close gross gains are not evidence for a costed next-open rule is the premise. A counterexample is the daily SOL, which remains positive, but hourly disagreement and missing required 3R, partial, and 15-minute policy prevent adoption. This short-hold toggle strategy is not a prior 10 low exit Donchian or managed ETH pullback. The conclusion is to reject this strategy as a live replacement. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the text describing a trading strategy for Bitcoin (BTC). The strategy uses a system called Vectorbt, which buys when the closing price exceeds the highest closing price of the prior 20 calendar days and sells when this condition is no longer true, using the exact opening price for the sale. This strategy was tested, or trained, from 2024 to 2025, starting on August 26, with the cash balance reset on January 1, and the results from 2026 up to September 12 were used to evaluate the strategy without interfering with it. The strategy includes fees of 25 basis points, or 0.25%, on each buy and sell, with a slippage of 10 basis points for buying and 30 basis points for selling. The daily high, low, open, and close prices were used from 24 complete hours of data, without filling in missing calendar days. The baseline training return was -2.113871%, with a drawdown of 26.189310%, 45 trades, and a win rate of 28.889%. The holdout return was -10.194852%, with a drawdown of 17.371016%, 18 trades, and a win rate of 22.222%, starting with no open positions. The stress test return was -16.433561%. A high threshold control had a holdout return of 5.123294% with 8 trades. All 15, 20, and 30-day close and high price cells were recorded from 26572 to 83, with a known holdout range of -17.855% to 5.689%, but none were selected. The premise is that same-close gross gains are not evidence for a costed next-open rule. A counterexample is the daily SOL, which remains positive, but hourly disagreement and missing required 3R/partial/15-minute policy prevent adoption. This strategy is not a prior 10 low exit Donchian or a managed ETH pullback. The conclusion is to reject this strategy as a live replacement. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline is not available, but a data line shows the results of a required CLI run. The run had the following returns: for BTC, the return was 46.460751%, the maximum drawdown was 14.692428%, the win rate was 34.328358%, and there were 67 trades; for ETH, the return was 41.521926%, the maximum drawdown was 24.943256%, the win rate was 31.481481%, and there were 54 trades; for SOL, the return was 72.151727%, the maximum drawdown was 18.795394%, the win rate was 52.777778%, and there were 36 trades. This was a diagnostic test, not an executable policy, using a same-close prior 20 day policy, with no fees and no holdout, and including a partial day on September 13. The managed ETH studies with IDs 2883, 2884, 2897, and 2899 were retained unchanged, with a training return of 42.312262%, a maximum drawdown of 37.521903%, and 7 wins out of 13 trades. The known 2026 holdout return was 19.206747%, the maximum drawdown was 14.542745%, and there were 3 wins out of 3 trades, with 1 open trade, and a stress test return of 17.549943%. The daily control return was -1.925730% over 2 trades. The policy used a 20 day high, then a half average true range pullback within 3 days, with a next exact open, a 2 average true range stop, and a 20 day timeout. The fees were 25 basis points, with a 10 basis point slip, and 15 minute point checks, with a quarter of the position at 2 times the risk, then a stop entry, and a residual target net 3 times the risk. The daily disagreement and training mean confidence interval was between -3.77% and 12.80%. The missing 15 minute bounds were retained from a prior audit and were not new quote proof. On September 11 at 22 UTC, an entry was made for ETH, but it did not result in a trade, with a size of 0. This record retries two failed writes, but neither failure was counted as a record. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the text describing the C91208 trading fund manager's notes. The manager is reusing experiments 2883, 2884, 2897, and 2899, which were previously audited. The policy for these experiments is to buy when the price breaks out above the highest price of the last 20 days, then pulls back by half of the average true range within 3 days. The manager then sets a stop loss at 2 times the average true range over 20 days and a timeout of 20 days. There is a 0.25% entry fee and a 1% exit fee. The manager also allows for a 0.1% slip on each side and a 0.3% stress test. The experiments showed a 42.31% return from 2024 to 2025, with a 37.52% drawdown, and 7 out of 13 trades were winners. In the known 2026 holdout period, the return was 19.21%, with a 14.54% drawdown, and all 3 trades were winners, with one trade still open. The stress test showed a 17.55% return. The daily training showed a -27.13% return, while the holdout period showed a -1.93% return. The manager notes that 18 missing 15-minute checks are not a concern. The manager concludes that there is no need for a new order and will preserve the current candidate without selecting new parameters or inflating the sample size. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A required command line interface audit was run with the strategy breakout, looking back 20 days, on the currencies Bitcoin/USD, Ethereum/USD, and Solana/USD. The results were: Bitcoin had a return of 46.460751%, a drawdown of 14.692428%, a win rate of 34.328358%, and 67 trades; Ethereum had a return of 40.761782%, a drawdown of 24.943256%, a win rate of 30.909091%, and 55 trades; Solana had a return of 72.151727%, a drawdown of 18.795394%, a win rate of 52.777778%, and 36 trades. The audit inspected the strategies, including the prior 20 close threshold, same close entry, no explicit fees, and no held out window, with the current partial daily bar included. Solana had 748 bars versus 981 major bars. The premise of the gross performance is diagnostic only. A counterexample was found in Ethereum, which had a high cost and a next open daily loss of 4.8672% and an hourly loss of 0.4895%, and these also lacked a managed 3R. The conclusion is that there will be no cross-policy borrowing of command line interface returns for orders. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars. The command line interface and raw statistics were saved in observations, but this is not an adoption decision. A news scan found the headline is not applicable here, this is a raw script output.
A news scan found the headline 'C91206 SOL hourly frozen Donchian comparator with known holdout and all sensitivity cells'. This is a report about an automated trading system. The system is based on two premises. The first premise is a trading rule that buys when the price breaks out above the highest price in the last 20 calendar days and sells when the price falls below the lowest price in the last 10 days. The system uses hourly data and considers fees of 25 basis points and a 10 basis point slip on each side, with a stress test of 30 basis points. The system was trained on data from 2024 to 2025, starting with SOL data from August 26, 2024, and the cash was reset on January 1. The system was tested on a holdout period from 2026, which was already researched, through September 12, 2026, at 06:00 UTC. The training period had a return of 9.334791%, a maximum drawdown of 36.795757%, 8 trades, a win rate of 50%, and a mean confidence interval of -4.938225% to 8.002702%. The holdout period had a marked return of 1.322187%, a maximum drawdown of 24.525746%, 4 closed trades, a win rate of 0%, a mean return of -5.897838%, and a confidence interval of -7.941673% to -3.062724%. There was also 1 open trade. The stress test return was -0.484906%. The second premise tested all combinations of 9 lookback periods and 3 simple moving average cells, with holdout base costs ranging from -2.935467% to 19.188094%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but this was not adjusted for the small sample size and multiple comparisons. One issue with this approach is that open profits can mask closed losses, and a strict rolling simple moving average becomes unavailable after gaps, so the number of trades is not a valid measure of performance. The conclusion is that no winning parameter was chosen from the holdout period and no current late entry is authorized. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees are described at https://docs.alpaca.markets/us/docs/crypto-trading. The report covers all 216 cell figures and observations from 26302 to 26325.
A news scan found the daily frozen Donchian comparator results for SOL. The premise was to buy when the price broke out above the high of the past 20 calendar days and sell when it fell below the low of the past 10 days, with a 25 basis point fee and 10 basis point slip on each side, and a stress test of 30 basis points. The daily high, low, open, and close prices were reconstructed from 24 complete hours. There were no stops, targets, or partial management, as this was a diagnostic comparator, not an executable policy. The training period was from 2024 to 2025, starting on August 26, 2024, with the cash reset on January 1, and the known holdout period was from 2026 through September 1, 2026, at 12:00 UTC. The train return was -10.120708%, with a drawdown of 42.459529%, 7 trades, a win rate of 42.857143%, and a mean confidence interval of -8.221019% to 5.529944%. The holdout period had a marked return of 4.581108%, a drawdown of 17.406778%, 3 closed trades, a win rate of 0%, a mean return of -4.373566%, and a confidence interval of -5.151662% to -3.875026%, with 1 open trade. The stress return was 3.12758%. All 9 lookback periods of 15, 20, and 30 days, with simple moving averages of 0, 100, and 200 days, were retained. The holdout base cost range was 0 to 19.561908%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, without adjusting for small sample size and multiple comparisons. A counterexample noted that marked open profits can mask closed losses, and strict rolling simple moving averages become unavailable after gaps, with 0 trades not being validation. The conclusion was that no winning parameter was chosen from the holdout, and no current late entry was authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are listed at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures and 26302-26325 observations were considered.
A news scan found the following trading results for Ethereum (ETH): the trading strategy used a Donchian comparator, which is a technical analysis tool, with a prior 20 calendar-day high breakout and a prior 10-day low exit. It also used a fill following the open, long cash only, with fees of 25 basis points and a slip of 10 basis points on each side, and stress of 30 basis points. The hourly strategy used 480 hours for the high and 240 hours for the low. There were no stops, targets, or partial management, as this was a diagnostic comparator, not an executable policy. The training period was from 2024 to 2025, with Sol starting on August 26, 2024, and the cash was reset on January 1. The known holdout period was from 2026 through September 12, 06:00 UTC, and the results for 2026 were already researched. The train return was 16.493028%, with a drawdown of 41.889093%, 14 trades, a win rate of 42.857143%, and a mean confidence interval of -6.826896% to 12.704494%. The holdout return was -0.489511%, with a drawdown of 22.644888%, 5 closed trades, a win rate of 40%, a mean of 1.326191%, and a confidence interval of -5.247297% to 10.986248%. The stress return was -2.654456%. All 9 lookback cells with 15, 20, and 30 periods, and SMA 0, 100, and 200 were retained. The holdout base cost range was -12.836788% to 13.949464%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but the results were not adjusted for small N and multiple comparisons. A counterexample noted that marked open profit can mask closed losses, and a strict rolling SMA becomes unavailable after gaps, and N0 is not validation. The conclusion was that no winning parameter was chosen from the holdout, and no current late entry was authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are explained at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures were observed from 26302 to 26325.
A news scan found the following text: Premise 1 is a preregistered trading strategy that buys when the price breaks out above the highest price in the last 20 calendar days and sells when the price falls below the lowest price in the last 10 days. It only buys at the next day's opening price and only uses cash, with fees of 25 basis points and a 10 basis point slip on each side, and a stress test of 30 basis points. The daily high, low, open, and close prices are reconstructed only from complete 24-hour periods. This strategy does not use stops, targets, or partial management, and is for diagnostic comparison only, not for actual use. The strategy was trained on data from 2024 to 2025, with the SOL data starting on August 26, 2024, and the cash was reset on January 1. The holdout period, which is a known period used to test the strategy, is from 2026 through September 1 at 12:00 UTC, and the results for 2026 were already researched and not new. The training period had a return of -24.66415%, a maximum drawdown of 50.545173%, 12 trades, a win rate of 41.666667%, and a mean confidence interval of -9.572975% to 7.246572%. The holdout period had a marked return of -4.867204%, a maximum drawdown of 17.451183%, 3 closed trades, a win rate of 0%, a mean return of -5.049061%, and a confidence interval of -9.724302% to -2.115529%. The stress test return was -6.189414%. Premise 2 retained all 9 combinations of 15, 20, and 30-day lookbacks and simple moving averages of 0, 100, and 200 days. The holdout base cost range was -12.839698% to 11.294846%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but the results are not adjusted for multiple comparisons. A counterexample is that an open profit can mask closed losses, and a strict rolling simple moving average becomes unavailable after gaps, and the number of trades is not validation. The conclusion is that no winning parameter was chosen from the holdout period and no current late entry is authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are described at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures are based on observations from 26302 to 26325.
A news scan found the following report: Premise 1 is a trading strategy that was planned in advance. It buys when the price of Bitcoin breaks out above the highest price of the last 20 calendar days, and sells when the price falls to the lowest price of the last 10 days. The strategy only buys and sells at the opening price of the next hour, and it only uses cash, with fees of 25 basis points and a 10 basis point slip on each side, and a stress test of 30 basis points. This strategy uses hourly data, with 480 hours for the high price and 240 hours for the low price. There are no stop-loss orders or profit targets, and the strategy does not manage positions partially. This is a diagnostic tool, not a strategy that can be used by a trader, and it is different from a previous strategy used for Ethereum. The strategy was tested on data from 2024 to 2025, starting on August 26, 2024, for the cryptocurrency SOL, and the cash was reset on January 1. The data from 2026, up to September 1, 06:00 UTC, was already known and used to test the strategy, and it is not new, unseen data. The return on investment during the test period was 30.449462%, with a maximum drawdown of 34.232668%, and 13 trades were made, with a win rate of 30.769231%, and a mean confidence interval of -5.327741% to 13.821846%. In the holdout period, the return was 17.935419%, with a maximum drawdown of 12.151445%, and 3 trades were closed, with a win rate of 33.333333%, and a mean of 0.4253%, and a confidence interval of -2.628821% to 6.359096%, and 1 trade was still open. The stress test return was 16.296286%. Premise 2 tested all combinations of 9 different lookback periods and 3 different moving average parameters, and retained all the results. The holdout period had a base cost range of 0 to 17.935419%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but the results were not adjusted for the small number of trades and multiple comparisons. One counterexample is that a marked open profit can hide closed losses, and a strict rolling moving average becomes unavailable after gaps, and the number of trades is not validation. The conclusion is that no winning parameter was chosen from the holdout period, and no current late entry is authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are described at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures are based on observations from 26302 to 26325.
A news scan found the headline 'C91206 BTC daily frozen Donchian comparator with known holdout and all sensitivity cells'. The report discusses a trading strategy for Bitcoin, denoted as BTC, which is a type of digital currency. The strategy is based on a premise that involves a 20-calendar-day high breakout and a 10-day low exit, with fees of 25 basis points and a slip of 10 basis points on each side, and a stress test of 30 basis points. A basis point is a unit of measurement, equal to 0.01%. The daily high, low, open, and close prices, referred to as OHLC, are reconstructed only from complete 24-hour periods. The strategy does not include stops, targets, or partial management, and is used for diagnostic comparison purposes only. The training period for the strategy is from 2024 to 2025, with the SOL, a type of cryptocurrency, starting on August 26, 2024. The cash is reset on January 1, and the holdout period, which is a period of time used to test the strategy, is from 2026 through September 1, 2026, at 12:00 UTC. The return on investment during the training period is 32.167365%, with a drawdown, which is a decline in value, of 32.754193%. The strategy had 10 trades, with a win rate of 30%, and a mean confidence interval, which is a measure of uncertainty, of -6.573376% to 17.652511%. During the holdout period, the return on investment is 18.257988%, with a drawdown of 10.252513%, and 2 closed trades, with a win rate of 50%. The mean confidence interval for the holdout period is -4.104569% to 4.199843%. The stress return, which is a measure of the strategy's performance during stressful market conditions, is 17.081765%. The report also discusses a second premise, which involves retaining all 9 lookback periods of 15, 20, and 30 days, with simple moving averages, referred to as SMA, of 0, 100, and 200 days. The holdout base cost range is from 0 to 18.257988%. The report concludes that no winning parameter was chosen from the holdout period, and no current late entry is authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are discussed at https://docs.alpaca.markets/us/docs/crypto-trading. The report includes 216 cell figures and 26302-26325 observations.