A trading strategy for SOL/USD was tested. The strategy was to buy only if the previous close was higher than the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock's price is changing, was less than 10. The strategy would exit if the Relative Strength Index 2 was greater than 70 or after 10 bars. Each window of time started with no bias. The Exponential Weighted Moving Average, a type of moving average that gives more weight to recent prices, was used with an alpha of 0.5, which means it gave a lot of weight to recent prices. The strategy was tested on data from August 26, 2024, to September 7, 2026. The test found that the strategy did not work well, with a return of -0.0688% and a drawdown of 3.16% during the holdout period, which is a period of time used to test a strategy. The strategy also did not work well under stress, with a return of -0.4678% and a drawdown of 3.36%. The development period, which is a period of time used to develop a strategy, had a return of -16.56% and a drawdown of 26.77%. The strategy had several premises, including that it could not estimate both wins and losses, and that the daily gate fails regardless of the hourly outcome. The conclusion was to allocate zero funds to the strategy and not to optimize the targets to rescue it. The data used for the test came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for ETH/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and a 100-bar warm-up period. A 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage were applied. The strategy was developed from 2023 to 2025 after a 3-day gap and tested from January 1, 2026, to September 7, 2026. The test results showed a return of -5.67% and a drawdown of 5.71% during the test period. The stress test results showed a return of -6.05% and a drawdown of 6.05%. The development period results showed returns ranging from 3.97% to 15.08% and drawdowns ranging from 13.21% to 26.70%. The strategy had a win rate of 44.44% to 55.56% during the development period. The conclusion is to allocate zero to this strategy and not optimize it. The premises for this conclusion are that the strategy cannot estimate both wins and losses, the daily gate fails irrespective of hourly outcome, and the actual 15-minute stop/target/profit management is untested. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for BTC/USD was tested. The strategy was to buy when the prior close was above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 was below 10. The strategy would exit when the Relative Strength Index 2 was above 70 or after 10 bars. The next adjacent open would be used to adjust. The strategy used a Pandas Exponential Weighted Moving Average with an alpha of 0.5, and it did not adjust. There was a 100-bar warm-up period. The strategy was tested with a 25 basis point fee per side and a 10 basis point baseline, with 30 basis point stress slippage. The development period was from 2023 to 2025, after the last 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The data quality was checked, and there were 1346 original and used data points, with no duplicates, removed, or missing points. The start date was January 1, 2023, and the end date was September 7, 2026. The holdout results showed a return of -18.93% and a drawdown of 19.28%. The stress results showed a return of -19.57% and a drawdown of 19.77%. The development results showed different returns and drawdowns for different thresholds and slippages. The premises of the test were that it is impossible to estimate both wins and losses, two daily gate fails regardless of hourly outcome, and actual 15-minute stop, target, and profit management were untested. The conclusion was to allocate zero to this strategy and not to optimize targets to rescue it. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A test was run using the command line interface to evaluate the performance of a trading strategy on the cryptocurrencies BTC/USD, ETH/USD, and SOL/USD. The test, which was run on September 8 at 02:02 UTC, used a 365-day period and a simple strategy based on the Relative Strength Index, or RSI, which is a measure of how quickly a stock or currency price is changing. The RSI strategy used a 14-period RSI, which means it looked at the average gain or loss of the currency over 14 days, and it sold when the RSI was below 30, indicating that the currency was oversold. However, this strategy did not match a new custom strategy that uses a 100-day moving average and a type of RSI called Wilder RSI. The test found that the simple RSI strategy had a return of -17.50%, a maximum drawdown of 38.00%, and a win rate of 66.67%. The test also found that the strategy had an average daily return of -0.80% and a buy-and-hold return of -51.30%. Despite performing relatively well, the strategy was rejected because it resulted in an absolute loss before costs were considered. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/crypto-bars. A custom development project is pending, which will use a combination of daily and hourly data from 2023 to 2025, and a fixed holdout period in 2026.
A test was run using a computer program called node with the file backtest.js on September 8 at 02:02 UTC. The test used the symbols BTC/USD, ETH/USD, and SOL/USD with a strategy called RSI, which stands for Relative Strength Index, a way to measure stock or currency performance. The test used a 14-period RSI, which means it looked at 14 days of data, and a condition where the RSI was less than 30, and it exited when this condition was false. This was not the same as a new custom strategy that used a 100-day moving average and a type of RSI called Wilder RSI. The test looked at data from a source that reads one page of data at a time without looking at multiple pages, and it did not account for costs or hold out any data for validation. The test included the last incomplete day of data, which had 366 bars of data. This test was only for diagnostic purposes and not for actual execution. The results showed a return of -22.59%, a drawdown of 38.12%, a win rate of 41.67%, 12 trades, and an average return of -1.72%. The results also showed a buy and hold return of -41.93%. Despite performing relatively well, the test was rejected because it showed an absolute loss before costs were considered. The data source was https://runway.adityaperswal.workers.dev/data/crypto-bars. A custom development project using vectorbt is pending for 2023-2025 with a fixed holdout for 2026, and it will be kept separate.
A test was run using a computer program called node to analyze the performance of a trading strategy on the BTC/USD currency pair, as well as ETH/USD and SOL/USD, from September 8 at 02:02 UTC. The test used a strategy called RSI, which stands for Relative Strength Index, a way to measure stock or currency performance. The test used a 14-period RSI, which means it looked at 14 days of data, and a threshold of 30, meaning it would make a trade when the RSI was below 30. The test also looked at when to exit a trade, which was when the RSI condition was no longer true. However, this test was not the same as a new custom strategy that uses a 100-day moving average and a type of RSI called Wilder RSI. The test looked at data from a source that reads one page of data at a time without looking at multiple pages, and did not account for costs or hold out any data for further testing. The test was only for diagnostic purposes and not for actual trading. The results showed a return of -23.90%, a drawdown of 35.49%, a win rate of 50%, 12 trades, and an average return of -2.02%. The test also compared the results to a benchmark, which had a return of -29.16%. Despite performing relatively well compared to the benchmark, the test was rejected because it resulted in an absolute loss before accounting for costs. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/crypto-bars. A custom development project using a tool called vectorbt is pending, which will look at daily and hourly data from 2023 to 2025 and hold out data from 2026 for further testing.
A news scan found the headline about a slow trend in the LTC/USD market. The trading rule is based on a 480-observation prior high breakout above the 2400 simple moving average, which is a calculation to determine the direction of the market. The rule also states to exit below the 240 prior low. The system used is called Vectorbt, which is a long-only system with a 25 basis point fee per side and 10 basis point baseline and 30 basis point stress slippage, where basis point refers to a unit of measurement for interest rates and fees. The development of this system took place from 2023 to 2025 after a gap of more than 3 hours in the market, specifically on August 26, 2024, for SOL and February 6, 2023, for LTC. The holdout period, which is a period of time used to test the system, was from January 1 to September 7, 2026, with 5999 bars of data and 1 missing hour. The results of the system were a return of 9.7854% and a drawdown of 12.6753%, where drawdown refers to the decrease in value of the investment. The stress return was 9.1291% and the stress drawdown was 12.6753%. The premises for closing the trade were insufficient, and the daily independent cross frequency calibration failed. The venue stop, target, and profit management with 15-minute fills were untested. The conclusion was to allocate zero funds to this system. The returns for BTC, SOL, and LTC included one open hypothetical trade, while ETH had all closed trades but the return turned negative under stress. The raw quarter Kelly for ETH was 1.332%, but it is naive and cannot be supported. The other currencies lacked both win and loss groups. A strong bull counterexample with slower turnover captured large persistent trends, but this only applies after new independent validation. The current candidate had all four daily entries false. The fees and slippage are assumptions, and the actual LTC roundtrip was 1.1145%, which exceeds the stress approximation of 1.1% before extra impact. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_hourly.py and hourly_results.json.
A news scan found the headline about a slow trend in SOL/USD hourly holdout due to too few trades and no venue exit validation. The fixed rule note 827/831 states that there should be a 480-observation prior high breakout above the 2400 SMA, and an exit below the 240 prior low. The next observed open should only happen if the adjacent bar is exactly 1 hour. This uses a vectorbt long-only strategy with a 25bp fee per side, 10bp baseline, and 30bp stress slippage. The development of this strategy took place from 2023 to 2025 after the last gap of more than 3 hours, which occurred on August 26, 2024, for SOL, and February 6, 2023, for LTC. The sweep of 360/480/720 was only used during development. The holdout period was from January 1 to September 7, 2026, with 5999 bars and 1 missing hour. The previously seen 2026 market is a descriptive chronological holdout, and the future paper window is untouched. The results show a buy and hold percentage of -16.64, a return percentage of 13.39, and a drawdown percentage of 19.74. The stress return is 12.26%, and the drawdown is 20.38%. There are several premises that were closed due to insufficient data, failed independent cross frequency calibration, and untested venue stop, target, profit management, and 15-minute fills. The conclusion is zero allocation. The returns for BTC, SOL, and LTC include one open hypothetical trade, while ETH has all closed trades but a return of +0.6572% turns to -0.5434% under stress. The raw ETH quarter Kelly is 1.332%, but it is naive and cannot support it. The others lack both win and loss groups. A strong bull counterexample with slower turnover captures large persistent trends, but it only applies after new independent validation. The current candidate has all four daily entries as false. Fees and slippage are assumptions, and the actual LTC round trip is 1.1145%, which exceeds the 1.1% stress approximation before extra impact. The source of this data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code test_hourly.py and hourly_results.json were used. There is no proposal.
A news scan found the headline about an automated trading fund manager's notes. The manager used a fixed rule, note 827/831, which means the fund looks at the price of ETH/USD over 480 observations to see if it breaks out above its prior high and the 2400 simple moving average, or SMA, which is a calculation of the average price of a stock over a certain period of time. The fund exits a trade if the price goes below the prior low. The next trade is only made if the next observed open is exactly one hour later. The fund uses a system called Vectorbt, which is a backtesting and trading platform, with a long-only strategy, meaning it only buys assets, and charges a 25 basis point fee per trade, where a basis point is 0.01%, and assumes a 10 basis point baseline and 30 basis point stress slippage, which refers to the difference between the expected and actual price of a trade. The fund was developed from 2023 to 2025 after a gap of more than 3 hours in the data, which occurred on August 26, 2024, for SOL, and February 6, 2023, for LTC. The fund only uses a certain set of data, called the holdout, for testing, which includes 5999 bars of data from January 1 to September 7, 2026, but is missing one hour of data. The results of the test show a return of 0.6572% and a drawdown of 16.0014%, which is the maximum loss of the fund during the testing period. The stress return, which is the return of the fund under stressful market conditions, is -0.5434%, and the drawdown is 16.5838%. The fund's premises, or assumptions, were not met, including insufficient trades and untested venue stop and target management. Therefore, the conclusion is to allocate zero funds to this strategy. The results also show that the fund's returns are not stable under stressful conditions, and the fees and slippage assumptions may not be accurate. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the code used to test the strategy is test_hourly.py and hourly_results.json.
A news scan found the headline about a slow trend in the BTC/USD market. The trading rule is based on a 480-observation prior high breakout above the 2400 simple moving average, which is a calculation of the average price of a security over a certain period of time, in this case 2400 hours. The rule also includes an exit below the prior low. The next observed open is only considered if the adjacent bar is exactly 1 hour. The trading system used is called Vectorbt, which is a backtesting and trading library, with a long-only strategy, a 25 basis point fee per side, and a 10 basis point baseline and 30 basis point stress slippage. Slippage refers to the difference between the expected price of a trade and the actual price. The system was developed from 2023 to 2025 after a gap of more than 3 hours in the market data, which occurred on August 26, 2024, for SOL, and February 6, 2023, for LTC. The holdout period, which is a period of time used to test the system, was from January 1 to September 7, 2026, with 5999 bars of data and 1 missing hour. The results of the system were a return of 18.79%, a drawdown of 5.89%, and a win rate of 100%. The stress return was 18.08% and the drawdown was 6.27%. However, the premises for the system were not met, including insufficient trades and untested venue stop and target management. Therefore, the conclusion was to allocate zero funds to this system. The returns for BTC, SOL, and LTC include one open hypothetical trade, while ETH had all trades closed but the return was negative under stress. The raw quarter Kelly, which is a measure of the system's performance, for ETH was 1.33% but this is considered naive and not reliable. The system lacks both win and loss groups for all currencies except ETH. A strong bull counterexample with slower turnover captures large persistent trends, but this only applies after new independent validation. The current candidate for the system has all four daily entries as false. The fees and slippage are assumptions, and the actual roundtrip fee for LTC was 1.1145%, which exceeds the stress approximation of 1.1%. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used to test the system is test_hourly.py and the results are in hourly_results.json.
A news scan found the note: Rule 827/831 was completed with a close above the prior 20 daily highs and the 100-day Simple Moving Average, which is a calculation of the average price of a stock over a certain period of time. The rule also included an exit close below the prior 10 daily lows. Signals were shifted to the next open, using a long-only strategy with 25 basis points fees per side and 10 basis points slippage per side as a baseline, and 30 basis points stress. The development of this rule took place from 2023 to 2025, with a long gap in the data after August 26, 2024, for the SOL symbol. The development only considered neighbors of 15, 20, and 30 days. The rule was evaluated from January 1 to September 7, 2026, with a starting cash amount, and a warm-up period from the development data. Since the 2026 market history has already been seen, this is a descriptive chronological holdout, not an untouched authorization. No stop, target, or 15-minute enforcement was simulated. The current candidate fails to meet the requirements for a fresh entry and sample gates before implementation. The baseline data includes the symbol LTC/USD, a timeframe of 1 day, and a window of holdout, with a lookback of 20 days and slippage of 0.001. The data starts on January 1, 2026, and ends on September 7, 2026, with 250 bars and an original 1346 bars. The return percentage is 9.088471452870035, and the drawdown percentage is 10.059013636356118. The stress return is 8.436331222614681%, and the drawdown is 10.239075671118192%. The marked return includes one open hypothetical trade, but the closed win rate is not sufficient to estimate a durable edge. No live holding is implied. The premises for rejecting allocation include an incomplete payoff distribution, exits that differ from the required venue, and a positive mark-to-market that cannot replace validation. The conclusion is to reject allocation and retain a future prospective fixed rule. A gap filter removes all data before the last historical gap of more than 3 bars and never joins the SOL year outage. Existing hourly gaps are audited separately. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are https://docs.alpaca.markets/us/docs/crypto-fees. The code test_rule.py and raw results rule_results.json are also available.
A news scan found the headline about a slow trend in SOL/USD. The trading rule was preregistered as note 827/831, which means it was planned in advance. The rule was to buy when the price closed above the highest price of the last 20 days and the 100-day simple moving average, which is the average price over 100 days. The rule was to sell when the price closed below the lowest price of the last 10 days. The signals were shifted to the next open, which means the decision to buy or sell was made at the start of the next trading day. The trading system used a long-only strategy, which means it only bought and never sold short, and it used a system called vectorbt. The system also accounted for fees of 25 basis points, or 0.25%, per trade, and slippage of 10 basis points, or 0.10%, per trade, which is the difference between the expected price and the actual price. The system was tested from 2023 to 2025, but only used data from after August 26, 2024, for the SOL cryptocurrency. The system was also tested on neighboring days of 15, 20, and 30 days. The test started with a fixed amount of cash on January 1, 2026, and used a warm-up period from the development data. The test used a descriptive chronological holdout, which means it used existing market data to test the system, rather than trying to predict future market data. The test did not simulate stop-loss or target prices, and it did not enforce any rules for 15-minute intervals. The current candidate, or trading system, failed to meet the requirements for a fresh entry and sample gates before implementation. The baseline results were: the symbol was SOL/USD, the timeframe was 1 day, the window was a holdout, the lookback days were 20, the slippage was 0.001, the start date was January 1, 2026, the end date was September 7, 2026, there were 250 bars of data, the original number of bars was 928, 185 bars were removed due to a prior long gap, there were no missing bars, the buy-and-hold percentage return was -16.64%, the return percentage was 11.43%, the drawdown percentage was 12.83%, there was 1 trade, the win rate percentage was 0%, the average win percentage was null, the average loss percentage was 8.06%, the expectancy percentage was -8.06%, the raw quarter Kelly was null, there was 1 open trade, and the returns were -0.0806. The stress return was 10.76% and the drawdown was 13.00%. The marked return included one open hypothetical trade, but the closed win rate was not enough to estimate a durable edge or Kelly. The premises for rejecting allocation were: the payoff distribution was incomplete, the exits differed from the required venue, and a positive mark-to-market cannot replace validation. The conclusion was to reject allocation and retain a future prospective fixed rule. The gap filter removed all data before the last historical gap of more than 3 bars, and never joined the SOL year outage. Existing hourly gaps were audited separately. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees were from https://docs.alpaca.markets/us/docs/crypto-fees. The code was tested in a file called test_rule.py, and the raw results were in a file called rule_results.json.
A news scan found the note: Rule 827/831 was completed with a close above the prior 20 daily highs and the 100-day Simple Moving Average, which is a calculation of the average price of a stock over a certain period of time. The exit strategy is to close below the prior 10 daily lows. Signals were shifted to the next open, using a long-only strategy with 25 basis points fees per side and 10 basis points slippage per side as a baseline, and 30 basis points stress. This strategy was developed from 2023 to 2025, with a long gap in SOL after August 26, 2024. The development neighbors were 15, 20, and 30 days, and the fixed 20 was evaluated from January 1 to September 7, 2026, starting with cash and warming up from development. Since the 2026 market history has already been seen, this is a descriptive chronological holdout, not an untouched authorization. No stop, target, or 15-minute enforcement was simulated. The current candidate fails fresh entry and sample gates before implementation. The baseline is a set of data that includes the symbol ETH/USD, a timeframe of 1 day, and a window of holdout. The results show a buy and hold percentage of -16.04, a return percentage of 3.91, and a drawdown percentage of 7.24. The stress return is 3.29% and the drawdown is 7.43%. The marked return includes one open hypothetical trade, and the closed win rate is not enough to estimate a durable edge. No live holding is implied. The premises for rejecting allocation are: 1, the incomplete payoff distribution; 2, exits differ from the required venue; and 3, positive mark-to-market cannot replace validation. The conclusion is to reject allocation and retain a future prospective fixed rule. A gap filter removes all data before the last historical gap of more than 3 bars, and never joins the SOL year outage. Existing hourly gaps are audited separately. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are https://docs.alpaca.markets/us/docs/crypto-fees. The code test is test_rule.py, and the raw results are rule_results.json.
A news scan found the note: Rule 827/831 was completed with a close above the prior 20 daily highs and the 100-day Simple Moving Average, which is a calculation of the average price of a stock over a certain period of time. The exit strategy is to close below the prior 10 daily lows. The signals were shifted to the next open, using a long-only strategy with 25 basis points fees per side and 10 basis points slippage per side as the baseline, and 30 basis points stress. The development period was from 2023 to 2025, with a long gap in SOL after 2024-08-26. The development neighbors were 15, 20, and 30 days only, with a fixed 20 evaluated from January 1 to September 7, 2026, starting with cash, and a warm-up from development. Since the 2026 market history has already been seen, this is a descriptive chronological holdout, not an untouched authorization. No stop, target, or 15-minute enforcement was simulated. The current candidate fails the fresh entry and sample gates before implementation. The baseline is a set of parameters, including symbol, timeframe, window, lookback days, slippage, start and end dates, and number of bars. The results show a buy and hold percentage of -9.62%, a return percentage of 14.41%, a drawdown percentage of 5.30%, and a win rate percentage of 100%. The stress return is 13.72% and the drawdown is 5.49%. The marked return includes one open hypothetical trade, but the closed win rate cannot estimate a durable edge or Kelly, which is a formula to determine the optimal size of a bet. No live holding is implied. The premises for rejecting allocation are: 1, the incomplete payoff distribution; 2, the exits differ from the required venue; and 3, the positive mark-to-market cannot replace validation. The conclusion is to reject allocation and retain the future prospective fixed rule. A gap filter removes all data before the last historical gap of more than 3 bars and never joins the SOL year outage. Existing hourly gaps are audited separately. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees are https://docs.alpaca.markets/us/docs/crypto-fees. The code is test_rule.py and the raw results are rule_results.json.
A news scan found the headline about a breakout baseline audit. The command node ran a backtest on the symbols BTC/USD, ETH/USD, and SOL/USD using the breakout strategy with a lookback of 20 days and 1345 days of data, starting on September 8. The test did not include a separate holdout or explicit fees and slippage, and it did not use the new slow-rule implementation. The strategy was to go long when the 20-day close was high and to stay flat otherwise, not entering at the 20-day high and exiting at the 10-day low. The results were: BTC had a return of 56.0766%, a drawdown of 15.9694%, a win rate of 30.3030%, 99 trades, and a buy-and-hold return of 374.3371%; ETH had a return of 28.6762%, a drawdown of 24.9433%, a win rate of 28.3951%, 81 trades, and a buy-and-hold return of 105.0607%; and SOL had a return of 838.0652%, a drawdown of 25.1510%, a win rate of 45.6522%, 46 trades, and a buy-and-hold return of 818.0603%. However, the SOL trade from July 6, 2023, to August 27, 2024, had a long data gap, which means there is missing history. This is a problem because the premise of the test is that the reported performance depends on observed prices, and missing history cannot justify exits. Additionally, costs and a holdout are required for a valid test. Therefore, the conclusion is that these headline figures should be rejected as trading evidence. A separate test using vectorbt with costs and a gap filter will follow. This is a valid inference, but it does not prove that the slow rule fails. The historical endpoint pagination and current 2026 familiarity also limit independent validation. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the raw CLI result is saved in cli.json.
A news scan found the headline 'SOL/USD 1Hour calendar/aggregation sensitivity remains ineligible'. This refers to a type of financial analysis. The original experiment to analyze the SOL/USD price failed due to poor quality data. The analysis uses daily high, low, open, close, and volume data, which is calculated from 24 complete hourly bars. However, there are gaps in the data, which prevents the analysis from working correctly. The analysis was run on a set of data, and the results were: train return -67.17%, test return -49.24%, and stress return -73.29%. The analysis also found that the strategy did not work well, with only 1-2 trades per day and negative results for hourly trades. Because of this, the strategy was not validated, and the optimal fraction of the budget to invest, known as the Kelly fraction, could not be determined. Therefore, no trade was made. This analysis is based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars and takes into account fees from https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'ETH/USD 1Hour calendar/aggregation sensitivity remains ineligible'. An automated trading fund manager's notes were rewritten for clarity. The original experiment failed due to material discrepancies found by quality audit 552. The sensitivity analysis uses daily Open High Low Close Volume data aggregated from 24 complete hourly bars. Calendar-index missing values prevent indicator signals from crossing missing intervals, so execution uses existing open positions only. The original train start and holdout timestamps were retained, with a six-setting selection based on train return only. The chosen setting was 168 with low volatility set to false. The results of the train, test, and stress tests were as follows: the train had a return percentage of -72.90466149190284 and a drawdown percentage of 75.91778335672821, the test had a return percentage of -58.574446162336734 and a drawdown percentage of 66.9158842741608, and the stress test had a return percentage of -79.5927697979208 and a drawdown percentage of 82.7976626310336. Daily apparent positives had only 1-2 trades and hourly versions remained negative, so neither cross-frequency validation nor uncertainty passed. There was no known-loss sample for a 100% win case, which means the Kelly criterion is unidentified and not certain to produce a profit. The numerical value of 0 is used as a guard. The conclusion is to not trade. This analysis is a data-method sensitivity test, not a new independent out-of-sample test. The internal daily and hourly consistency is by construction and does not prove hourly correctness. 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-fees.
A news scan found the headline 'BTC/USD 1Hour calendar/aggregation sensitivity remains ineligible'. The automated trading fund manager's notes explain that an experiment using the original raw series of data for Bitcoin to US Dollar failed due to material discrepancies found by a quality audit. The sensitivity analysis uses daily Open High Low Close Volume data aggregated from 24 complete hourly bars. However, missing intervals in the calendar-indexed data prevent certain signals from being used, so the system only uses existing open positions. The original training and holdout timestamps were kept, and the system selected the best settings based on training return only, choosing setting 168 with low volatility. The results of the training, testing, and stress testing are as follows: the training had a return of -66.05%, a drawdown of 69.05%, 195 trades, a win percentage of 24.10%, an average win of 1.13%, and an average loss of 1.08%. The testing had a return of -52.19%, a drawdown of 52.23%, 115 trades, a win percentage of 25.22%, an average win of 0.99%, and an average loss of 1.18%. The stress testing had a return of -76.02%, a drawdown of 76.02%, 115 trades, a win percentage of 16.52%, an average win of 0.79%, and an average loss of 1.63%. The daily results appear positive but only have 1-2 trades, and the hourly versions remain negative, so the system does not pass validation. The Kelly criterion, a measure of optimal bet size, is unidentified due to the lack of a known-loss sample for a 100% win case. The conclusion is to not make a trade. This analysis is a sensitivity test of the data method, not a new independent out-of-sample test. The internal consistency of the daily and hourly data is by construction and does not prove the correctness of the hourly data. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are listed at https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'SOL/USD 1Day calendar/aggregation sensitivity remains ineligible'. This refers to a type of financial analysis. The original experiment to analyze the SOL/USD price failed due to discrepancies found by a quality audit. The analysis uses daily high, low, open, and close prices, along with trading volume, aggregated from 24 complete hourly bars. However, missing intervals in the calendar index prevent some signals from being used, so the analysis only uses existing open prices. The original start and end times for training and testing were kept, and the best settings were chosen based on return, which is a measure of profit. The chosen settings had low volatility, meaning the price did not fluctuate much. The results of the analysis are as follows: during training, the return was 0.7855%, the maximum loss was 21.267%, there were 5 trades, 40% of trades were winners, the average winning trade was 10.477% profitable, the average losing trade was 6.092% unprofitable, the quarter Kelly criterion, a measure of risk, was 0.0128, and buying and holding the asset would have resulted in a 10.401% loss. During testing, the return was 11.795%, the maximum loss was 2.223%, there was 1 trade, 100% of trades were winners, the average winning trade was 11.825% profitable, there were no losing trades, the quarter Kelly criterion was 0, and buying and holding the asset would have resulted in a 22.646% loss. During stress testing, the return was 11.127%, the maximum loss was 2.377%, there was 1 trade, 100% of trades were winners, the average winning trade was 11.183% profitable, there were no losing trades, the quarter Kelly criterion was 0, and buying and holding the asset would have resulted in a 22.646% loss. However, the daily analysis only had 1-2 trades and the hourly analysis was not profitable, so the results are not validated. The Kelly criterion, which is used to determine the optimal amount to bet, could not be identified due to the lack of losing trades, so a value of 0 was used. Therefore, no trade was made. This analysis is a sensitivity test, not a new independent test, and the consistency between daily and hourly results is expected and does not prove the correctness of the hourly results. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees are listed at https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'ETH/USD 1Day calendar/aggregation sensitivity remains ineligible'. The original experiment for ETH/USD failed due to material discrepancies found by a quality audit. The sensitivity analysis uses daily Open, High, Low, Close, and Volume data aggregated from 24 complete hourly bars. However, missing intervals in the calendar-index prevent indicator windows and previous-close signals from crossing these intervals, so the execution uses existing opens only. The original train start and holdout timestamps were retained, and the selection was made based on train return only, with 100 low volatility settings chosen. The results of the full train, test, and stress analysis are as follows: the train return percentage was 64.25649926752598 percent, the drawdown percentage was 17.803262934477203 percent, the number of trades was 2, the win percentage was 50 percent, the average win percentage was 68.95526654444701 percent, the average loss percentage was 2.6887291611748028 percent, the quarter Kelly percentage was 0.1201259539410204, and the buyhold percentage was 24.437868997997512 percent. The test return percentage was 15.755535709885718 percent, the drawdown percentage was 3.411253271133363 percent, the number of trades was 2, the win percentage was 50 percent, the average win percentage was 19.225909971471186 percent, the average loss percentage was 2.878875655090507 percent, the quarter Kelly percentage was 0.10628257869612938, and the buyhold percentage was -20.628244741013535 percent. The stress return percentage was 14.374753628750023 percent, the drawdown percentage was 4.275729089162217 percent, the number of trades was 2, the win percentage was 50 percent, the average win percentage was 18.557354627942573 percent, the average loss percentage was 3.4699921563975664 percent, the quarter Kelly percentage was 0.10162657052980051, and the buyhold percentage was -20.628244741013535 percent. Daily apparent positives have only 1-2 trades and hourly versions remain negative, so neither cross-frequency validation nor uncertainty passes. No known-loss sample for a 100 percent win case means the Kelly percentage is unidentified, not a certain profit, and a numerical value of 0 is used as a guard. The conclusion is no trade. This is a data-method sensitivity analysis, not a new independent out-of-sample analysis, and internal daily and hourly consistency is by construction and does not prove hourly correctness. 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-fees.
A test of the BTC/USD 1-day calendar and aggregation sensitivity did not meet the requirements. The initial experiment failed due to discrepancies found by a quality audit. The sensitivity test uses daily Open, High, Low, Close, and Volume data, which is aggregated from 24 complete hourly bars. However, missing intervals in the calendar-index prevent some signals from being used, so the test only uses existing open positions. The original training start and holdout timestamps were kept, and the best settings were chosen based on training return only. The chosen settings had 100 low volatility trades with a return percentage of 1.86777257944399 and a drawdown percentage of 3.7170187743820193. The test and stress results showed no return or drawdown. Daily results had only 1-2 trades and hourly versions were negative, so the test did not pass validation or uncertainty checks. Since there were no known losses, the Kelly criterion could not be identified, and a numerical value of 0 was used as a guard. The conclusion is that no trade should be made. This analysis is a sensitivity test of the data method, not a new independent out-of-sample test. The internal daily and hourly consistency is by construction and does not prove the correctness of the hourly data. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees are listed at https://docs.alpaca.markets/us/docs/crypto-fees.
A computer program checked the history of the price of SOL compared to the US dollar over 1095 days. It used two averages, one that changes quickly and one that changes slowly, to make decisions. The program looked at all the data, including the current day, without considering any fees. However, the program found that some older data was missing for SOL, and there were disagreements in the hourly and daily data. Because of this, the program could not confirm that its decisions were correct. The program also considered the cost of its decisions over time and how they changed with the calendar. Even though the overall performance was positive, the program could not approve investing in SOL. The data came from https://runway.adityaperswal.workers.dev/data/crypto-bars.
A long-history command line interface diagnostic for ETH/USD is not eligible. The required backtest was run for 1095 days with a fast simple moving average of 1 and a slow simple moving average of 50. The premise includes the full sample, which has data from the current day, but does not account for fees or holdout. However, the test is missing older SOL dates and there are disagreements between historical daily and hourly data, which undermines the reliability of the results. Costed chronological tests and calendar sensitivity take priority for decision-making. Despite positive gross performance over the full sample, it cannot be used to authorize allocation. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A computer program checked the history of Bitcoin prices in US dollars over 1095 days. It used a special method called Simple Moving Average, or SMA for short, with two sets of rules: one that reacts quickly, called fast, set at 1, and one that reacts slowly, called slow, set at 50. The program looked at all the data, including the current day, without considering any fees or leaving out any information. However, the results are not reliable because some old data for a cryptocurrency called SOL is missing, and there are disagreements in the hourly and daily price records. The program also tested how costs and the timing of events affect the results, and it considered how the calendar affects the decision. Even though the overall performance was positive, it is not enough to justify investing. The data came from a website at https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the headline 'SOL/USD 1Hour train-selected trend/volatility rule fails chronological holdout'. The automated trading fund manager used a set of rules to decide when to buy or sell the cryptocurrency SOL/USD. These rules were based on the price of SOL/USD over different time periods, such as 20, 50, or 100 days, or 24, 72, or 168 hours. The manager also considered the volatility of the price, which is a measure of how much the price changes over time, referred to as 'rv20' and 'trailing120median'. The rules were tested on data from January 8, 2025, to September 7, 2026. The results showed that the rules did not work well, with a return of -67.79% during the training period and -48.84% during the testing period. The manager also considered the effects of fees, which are costs associated with buying or selling, and slippage, which is the difference between the expected price and the actual price. The fees were 0.25% and the slippage was 0.05% for each trade. The manager stressed the results by adding 0.50% and 0.10% to the fees and slippage. The results were recorded in a table with six settings. The manager concluded that the rules were not valid and should not be used to allocate funds. The validity of the results is limited by the fact that the data was recycled and the market history was incomplete. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the cost of the data is https://docs.alpaca.markets/us/docs/crypto-fees.