Stats

A frozen protocol, numbered 1388-1389, is in place. OEF is the primary investment, while SPY and IWM are used for comparison. The training period for this investment strategy was from 2016 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026. A common 20-session warmup period is used, excluding January 2016. The third week is determined by the calendar Friday ordinal, with holidays using the last actual session. The investment strategy uses a managed stop, with a lagged 20-session average daily range and a target of 1.5 times the average daily range. The stop loss is adjusted after a +1 average daily range high. A news scan found the headline 'train managed week3 6bps'. The investment strategy is long-only, with a fee of 1 basis point plus 5 basis points slippage per side, and 1000 USD is allocated to each independent sleeve. The results for OEF and the comparison figures are based on 59 samples, with a win rate of 52.54% for SPY and IWM. The mean return for OEF is not provided, but the mean return for SPY is 0.17829139115395584 and for IWM is -0.042651513928642346. A data line found the following statistics for SPY and IWM: win rate, mean return, average win, average loss, quarter Kelly criterion, worst return, total return, drawdown, exit counts, and mean confidence interval percentage. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.sciencedirect.com/science/article/pii/S0378426613003051.

A frozen protocol, numbered 1388-1389, is in place. OEF is the primary fund, while SPY and IWM are comparison funds, not alternatives chosen for better returns. The training period for this fund is from 2016 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026. The training uses complete windows, with entries and exits only within these windows. A common 20-session warmup period removes January 2016 from consideration. The week is measured by the calendar Friday ordinal, with holidays using the last actual session. The parent fund's performance is measured from the last close of the previous week to the last close of the current week. The open is measured from the first open to the last close. A managed stop is in place, with a lag of 20 sessions, using the average daily range, or ADR, as a target, set at 1.5 times the ADR, with narrow and wide ranges set at 0.75 and 1.25 times the ADR, respectively. If the fund reaches a high of +1 ADR, the stop is raised at the next session. The stop is also subject to ambiguity at the open, with adverse gaps at the open and favorable gaps capped at the target. The time exit is set at the last close. The fund uses a long-only strategy, with fees of 1 basis point, or bp, plus 15 bp slippage per side, and 1000 USD is allocated to each independent fund, with idle cash set at 0. Every final cash return is reconciled with compounded cash trade returns and trade count. Drawdown is observed at open and close marks, not full intraday. The fund does not claim live 15-minute fills, cash-dividend accounting, partial-taking, or whole-lot replication. The mean confidence interval, or CI, is from 10,000 quarter-block samples, not independent daily or cross-asset observations. The holdout period has been historically reused across other strategies, not pristine research-wide out-of-sample, or OOS. The OEF figures are below, along with comparison figures and an equal-sleeve portfolio. A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all and https://www.sciencedirect.com/science/article/pii/S0378426613003051.

A frozen protocol, numbered 1388-1389, is in place. OEF is the primary investment, while SPY and IWM are used for comparison. The training period for this investment strategy is from 2016 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026. The strategy uses a 20-session warmup period, excluding January 2016. The third week is determined by the calendar Friday. If there is a holiday, the last actual trading session is used. The strategy involves managing stops, which are adjusted based on the average daily range, or ADR, which is a measure of how much a stock's price moves on average each day. The stop loss is set at 1.5 times the ADR, and is adjusted based on the stock's performance. The strategy also involves a breakeven point, where the stop loss is raised to the next session after the stock reaches a high of +1 ADR. The strategy is long-only, meaning it only buys stocks, and does not sell short. The fees are 1 basis point, or 0.01%, plus 5 basis points, or 0.05%, for slippage per trade. Each sleeve, or investment portfolio, starts with 1000 USD, and idle cash is set to 0. The returns are compounded, and the drawdown, or loss, is observed at the open and close of each trading session. A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all and https://www.sciencedirect.com/science/article/pii/S0378426613003051. The results for OEF and the comparison investments are as follows: OEF had a return of 11.762449245721553%, with a drawdown of 10.160487883635405%, while SPY had a return of 11.762449245721553%, with a drawdown of 10.160487883635405%, and IWM had a return of 10.906360404161205%, with a drawdown of 19.263304699390115%.

A frozen protocol, numbered 1388-1389, is in place. OEF is the primary investment, while SPY and IWM are used for comparison. The training period for this investment strategy is from 2016 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026. A common 20-session warmup period is used, which removes January 2016 from consideration. The week is defined by the calendar Friday, and holidays use the last actual trading session. The parent strategy looks at the previous week's last close to the current week's last close, and the open from the first open to the last close. A managed stop is used, which is lagged by 20 sessions, with a target of 1.5 times the average daily range, and is narrowed or widened based on certain conditions. If the investment reaches a certain level of return, the stop is raised the next session. The strategy uses a vectorbt explicit long-only approach, with a fee of 1 basis point plus 15 basis points of slippage per side, and each independent sleeve starts with 1000 USD. The final cash return is reconciled with the compounded cash trade returns and trade count. Drawdown is observed at the open and close marks, not at the full intraday level. A news scan found the headline is not applicable here, instead, the source is a data output. The results for OEF and the comparison figures are as follows: the SPY had a sample size of 59, a win rate of 54.24%, a mean return of -0.13%, an average win of 1.13%, an average loss of 1.62%, and a worst return of -14.87%. The IWM had a sample size of 59, a win rate of 59.32%, a mean return of -0.07%, an average win of 1.56%, an average loss of 2.46%, and a worst return of -15.81%. The portfolio return was -8.46% and the portfolio drawdown was 24.38%. All data points were recorded in matching observations. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all and https://www.sciencedirect.com/science/article/pii/S0378426613003051.

A frozen protocol, numbered 1388-1389, is in place. OEF is the primary fund, while SPY and IWM are comparison funds, not alternatives chosen for better returns. The training period for this fund is from 2016 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026. The training period is divided into complete windows, with entries and exits only within those windows. A common 20-session warmup period removes January 2016 from consideration. The week is defined by the calendar Friday ordinal, with holidays using the last actual trading session. The parent fund's performance is measured from the last close of the previous week to the last close of the current week. The open is measured from the first open to the last close. A managed stop is in place, with a lagged 20-session average daily range, or ADR, and a target of 1.5 times the ADR. The stop is narrowed to 0.75 and 1.125 times the ADR, and widened to 1.25 and 1.875 times the ADR. If the fund reaches a high of +1 ADR, the stop is raised to the next session. If there is a stop-first open, high, low, or close ambiguity, or an adverse gap at open, or a favorable gap capped at the target, the exit is at the last close. The Vectorbt system is used, with an explicit long-only strategy, a fee of 1 basis point, plus 5 basis points of slippage per side, and 1000 USD for each independent fund. Idle cash is set to 0. Every final cash return is reconciled with compounded cash trade returns and trade count. Drawdown is observed at open and close marks, not full intraday. There are no live 15-minute fills, cash-dividend accounting, partial-taking, or whole-lot replication. The mean confidence interval, or CI, is from 10,000 quarter-block samples, not independent daily or cross-asset observations. The holdout period has been historically reused across other strategies, not pristine research-wide out-of-sample. The OEF figures are below, along with comparison figures and an equal-sleeve portfolio. A news scan found the data: OEF, SPY, and IWM have the following performance metrics, including sample size, win rate, mean return, average win, average loss, quarter Kelly criterion, worst return, return, drawdown, exit counts, and ambiguous counts. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all and https://www.sciencedirect.com/science/article/pii/S0378426613003051.

A trading fund manager preregistered 1386 events before outcomes. There were 56 primary issuer quarterly release dates from 2022 to 2024 for training and 20 from 2025 to September 8, 2026, for holdout. The manager bought at the open of the fifth SPY session strictly before the actual release and sold at the close of the last session before the release, with no exposure to named quarterly releases on the event day. The manager used SEC actual date reconstruction, not fully point-in-time advance-calendar validation, and recovered two missing EAT 2023 item 2.02 metadata entries from 9.01 files. Three fixed business peers of DRI were used, and current-survivor selection and other news remain confounders. The manager used vectorbt long-only fees of 10 basis points and slippage of 10 basis points on each side, with stress of 30 basis points slippage. Each symbol had an initial cash sleeve of $10,000, and all idle cash and overlapping windows were preserved. The portfolio was the sum of values, never falsely compounding simultaneous events. A news scan found the headline from https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all. The results were as follows: TRAIN had 12 events, a mean percentage of -0.6708, a win rate of 33.33%, an average win of 6.26%, and an average loss of 4.14%. HOLDOUT had 6 events, a mean percentage of -4.21, a win rate of 16.67%, an average win of 0.75%, and an average loss of 5.21%. STRESS had 6 events, a mean percentage of -4.60, a win rate of 16.67%, an average win of 0.35%, and an average loss of 5.58%. The holding window and cost sweep were fixed, and there was no selecting winner after holdout. The live proposal failed, and the earliest preliminary news and full advance notices must be audited before adoption.

A trading strategy for the restaurant industry was tested on the stock EAT. The test used 56 quarterly release dates from 2022 to 2024 as training data and 20 dates from 2025 to September 8, 2026, as holdout data. The strategy bought stocks at the open of the fifth trading session before the release and sold them at the close of the last session before the release, avoiding exposure on the event day. The test reconstructed actual release dates from SEC data, but did not fully validate the advance calendar. Three business peers of DRI were used as fixed comparators. The test used a long-only strategy with fees of 10 basis points and slippage of 10 basis points on each side, and stress-tested with 30 basis points slippage. The results showed a mean return of 2.25% and a win rate of 66.67% in the training period, but a mean return of -2.85% and a win rate of 14.29% in the holdout period. The stress test showed a mean return of -3.24% and a win rate of 14.29%. The test was not adopted due to negative diagnostic results.

A news scan found the headline about a restaurant transfer. Before outcomes, 1386 preregistered. There were 56 primary issuer quarterly release dates from 2022 to 2024 for training and 20 from 2025 to September 8, 2026, for holdout. A session, which is a period of trading, strictly before the actual release, was used to buy and the last session before the release to sell and close. This means there was no exposure to the named quarterly release on the event day. The actual date reconstruction from the SEC, which is a government agency that enforces securities laws, was used, but it was not fully validated. Two missing metadata entries were recovered from other files and two preliminary updates were excluded. Three fixed business peers of DRI, which is a company, were used. Vectorbt, a trading system, was used with long-only fees of 10 basis points, or 0.1%, and slippage of 10 basis points. Each symbol, or stock, had an initial cash sleeve of $10,000. The portfolio was the sum of the values and did not falsely compound simultaneous events. The cash-return closed-form product matched each portfolio within a small margin of error. An initial default direction error was caught and corrected. Daily marking drawdown, which assumes fractional sizing, was used. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars. The results were as follows: TRAIN had a mean percentage of -0.8473%, a win rate of 33.33%, and a drawdown of 11.46%. HOLDOUT had a mean percentage of 0.4673%, a win rate of 57.14%, and a drawdown of 5.76%. STRESS had a mean percentage of 0.0663%, a win rate of 42.86%, and a drawdown of 6.52%. The holding window and cost sweep were fixed. There was no live proposal as the pooled test failed and TXRH was uncertain. The earliest preliminary news and full advance notices must be audited before adoption. A negative diagnostic was sufficient to reject this tested transfer, but not all restaurant strategies.

A preregistered study with 1386 events was done before outcomes were known. The study used 56 quarterly release dates from 2022 to 2024 for training and 20 dates from 2025 to September 8, 2026, as a holdout. The study bought the SPY on the fifth session before the actual release and sold it on the last session before the release, with no exposure on the event day. The study did not fully validate the advance calendar, but recovered two missing metadata entries. Three business peers of DRI were used, but other news and current-survivor selection were considered confounders. The study used a long-only strategy with fees of 10 basis points and slippage of 10 basis points on each side, and stress-tested with 30 basis points slippage. Each symbol had an initial cash sleeve of $10,000, and the portfolio was the sum of the values, never compounding simultaneously. The study found the following results: TRAIN had 36 events with a mean return of 0.2424%, a win rate of 44.44%, and a drawdown of 9.96%. HOLDOUT had 20 events with a mean return of -2.0993%, a win rate of 30%, and a drawdown of 15.14%. STRESS had 20 events with a mean return of -2.4901%, a win rate of 25%, and a drawdown of 16.94%. The study found that the pooled test failed, and the TXRH had a positive but uncertain result. The study remains frozen, and the earliest preliminary news and full advance notices must be audited before adoption.

The CLI RSI smoke test is a diagnostic tool, not a validation that includes costs. A test was run using the node /app/agent/dist/backtest.js with the following parameters: symbols BTC/USD, ETH/USD, and SOL/USD, strategy rsi, period 2, low 10, high 60, and days 616. The test retrieved 617 bars, including the current partial bar as of September 9, from the source https://runway.adityaperswal.workers.dev/data/crypto-bars. The results showed an uncosted return of -14.5454% for BTC, +53.1478% for ETH, and -28.7169% for SOL, with a maximum drawdown of 33.2792% for BTC, 40.4938% for ETH, and 43.7119% for SOL. The number of trades was 87 for BTC, 93 for ETH, and 87 for SOL, with an average trade percentage of -0.14368 for BTC, 0.57454 for ETH, and -0.29181 for SOL. A buy and hold strategy would have resulted in a return of -16.4412% for BTC, -25.5942% for ETH, and -46.3200% for SOL. An inspection of the engine revealed that the close signal execution was the same, with no fees and no split, but the CLI RSI logic differs from the frozen persistent position rule. Therefore, the 53% return for ETH cannot be used to justify trading. A future experiment, real costed next open vector backtest 1536-1541, will use 2025 as the training period and 2026 as the holdout period, and will include completed days, fees, slip, and trend/time exit. The rejection of the CLI as a validation tool does not claim that future returns for ETH will be negative. The statistics provided are for an illustrative uncosted diagnostic test, not for actual fund performance, and come from the local CLI engine and https://docs.alpaca.markets/us/docs/crypto-fees.

A diagnostic test was run on the HUBB stock using a command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the HUBB and WCC stocks. The strategy used was a breakout strategy, which means it looks for stocks that are breaking out of a certain price range, and it looked back at 20 days of data and ran the test for 730 days. The test also used a stop loss, which is a rule to sell a stock when it falls by a certain percentage, in this case 3%, and a target profit, which is a rule to sell a stock when it rises by a certain percentage, in this case 4.5%. The results of the test were: the stock HUBB had 501 data points, a return of -10.145%, a buy and hold return of 22.815%, a maximum drawdown of 19.799%, a win rate of 34.210%, and an average trade return of -0.259%. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test showed that the results from the command line interface were different from the results from another source, IEX, and that the costs of buying and selling the stock were not taken into account. This test was not meant to validate the costs of holding the stock for a certain period of time, and the results of the test were recorded separately. The qualification for the stock remains rejected.

A diagnostic test was run on the stock symbol WCC using a command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the symbols WCC and HUBB. The strategy used was a breakout strategy, which means buying a stock when its price breaks out of a certain range, and it looked back at 20 days of data over a period of 730 days. The test also used a stop loss, which is a rule to sell a stock when it loses a certain percentage of its value, in this case 3%, and a target profit, which is a rule to sell a stock when it gains a certain percentage of its value, in this case 4.5%. The results of the test were: for the symbol WCC, there were 501 bars of data, which is a unit of time, and the return on investment was -17.52%, while the return on investment from buying and holding the stock was 135.55%. The maximum drawdown, which is the largest loss of value, was 21.15%, the win rate, which is the percentage of successful trades, was 31.82%, and the average trade return was -0.42%. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test showed that the results from the command line interface were different from the results from another source, IEX, and that the costs of the trades were not explicitly modeled. This means that the test did not take into account the costs of buying and selling the stocks. The test was recorded separately and the qualification, which is a measure of how well the strategy performed, was rejected.

A news scan found the headline about SOL 1Hour trend-pullback holdout having insufficient independent trades. The note starts with a frozen note1384 before fetching. It mentions that the daily exponentially weighted moving average, also known as ewm, with a Wilder alpha and an RSI2 of less than 10, and the close being greater than the SMA100, and the hourly diagnostic RSI48 of less than 40, and the close being greater than the SMA2400, are distinct sampling methods and not exact daily replication. The next open entry and exit will be the next open after the RSI is greater than 60 daily or greater than 50 hourly, or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was in September 2024, the training period was in 2025, and the out-of-sample, or OOS, period was from January 1 to September 8, 2026, which was held out for this rule but reused the calendar from prior research, which is not pristine independent confirmation. The fees for entry and exit are 0.0025 and 0.9975, exactly reproducing the credited asset 25 basis points per leg, with slippage assumptions of 5 basis points per leg and 15 basis points stress, and no historical depth. The portfolio is an isolated 100 cash fully invested portfolio. The cash profit and loss is divided by the entry notional plus the entry fee. All 144 compounded payoffs reconcile to 1e-7 percentage points. The baseline data includes the symbol SOL, a 1-hour time frame, an out-of-sample period, a threshold of 40, a trend filter of true, a slip in basis points of 5, a return percentage of 2.0571309885728795, a drawdown percentage of 5.428983945916399, a number of trades of 3, a win percentage of 66.66666666666666, an average win percentage of 2.928592480689437, an average loss percentage of 3.6656875267508515, an expectancy percentage of 0.7304991448760073, a lower confidence interval percentage of -3.6656875267508515, an upper confidence interval percentage of 3.415400829960636, a quarter Kelly of 0.062359234828059454, and held missing of 0. The training return was -23.49450244882479 percent, with an N of 10 and an expectancy of -2.566572205239217 percent, and held missing of 6. The stress out-of-sample return was 1.446620903042998 percent. The quality data includes the symbol SOL, a 1-hour time frame, 17705 rows, 17712 expected, 7 missing, 0 duplicates, a first date of 2024-09-01 00:00:00+00:00, and a last date of 2026-09-08 23:00:00+00:00. There are no baseline out-of-sample invested gaps, and forward fill for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, with N1 bounds degenerate and not population uncertainty. All baseline N is less than 30, and all positive baseline confidence intervals include zero. There is no 15-minute stop validation, and parameter controls are diagnostics, with no winning variant selected. The full 144 rows of observations are from 17457 to 17544. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline about SOL 1Day trend-pullback holdout having insufficient independent trades. The note starts with a frozen note1384 before fetching. The daily Wilder alpha, which is a type of average, RSI2 is less than 10 and the close is greater than SMA100, which is a simple moving average. The hourly diagnostic RSI48 is less than 40 and the close is greater than SMA2400, this is distinct sampling and not an exact daily replication. The next open entry and exit will be the next open after RSI is greater than 60 daily and greater than 50 hourly or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was September 2024, training was in 2025, and the out-of-sample period was from January 1 to September 8, 2026, which was held out for this rule but reused the calendar from prior research, which is not pristine independent confirmation. The fees for entry and exit are 0.0025 and 0.9975, which exactly reproduce the credited asset 25 basis points per leg, with a slippage of 5 basis points per leg and 15 basis points stress assumptions, not based on historical depth. The portfolio is an isolated 100 cash fully invested portfolio. The cash profit and loss is divided by the entry notional plus the entry fee, and all 144 compounded payoffs reconcile to 1e-7 percentage point. The baseline results show a return of -3.445115910082066 percent, a drawdown of 3.445115910082064 percent, and an expectancy of -3.4451159100820625 percent. The training return was -18.19790734435226 percent, with an expectancy of -2.5331341375826337 percent. The stress out-of-sample return was -3.638032905953807 percent. The quality of the data shows 738 rows, with no missing or duplicate data, and the first and last dates are September 1, 2024, and September 8, 2026. There are no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, but the N1 bounds degenerate and do not show population uncertainty. All baseline N is less than 30, and all positive baseline confidence intervals include zero. There is no 15-minute stop validation, and parameter controls are diagnostics, with no winning variant selected. The full 144 rows of observations are from 17457 to 17544. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline about an automated trading fund manager's notes. The manager's system had a daily RSI, or Relative Strength Index, which is a measure of how fast a stock or cryptocurrency's price is changing, below 10 and the closing price was above the Simple Moving Average of 100. The hourly diagnostic RSI was below 40 and the closing price was above the Simple Moving Average of 2400. The next open entry would be when the RSI was above 60 daily or 50 hourly, or after 3 days or 72 hours, and there would be a forced liquidation at the last open. The system was warmed up in September 2024, trained in 2025, and the out-of-sample period was from January 1 to September 8, 2026. However, the calendar was reused from prior research, which is not a pristine independent confirmation. The fees for entry and exit were 0.0025 and 0.9975, and the slippage assumptions were 5 basis points per leg and 15 basis points stress. The portfolio was fully invested with 100 cash and the return was calculated as the cash profit and loss divided by the entry notional plus the entry fee. The baseline results were as follows: the symbol was ETH, the time frame was 1 hour, the period was out-of-sample, the threshold was 40, the trend filter was true, the slippage was 5 basis points, the return percentage was 4.4865, the drawdown percentage was 5.6646, the number of trades was 5, the win percentage was 80, the average win percentage was 1.8956, the average loss percentage was 3.0733, the expectancy percentage was 0.9018, the lower confidence interval percentage was -1.1393, the higher confidence interval percentage was 2.0648, and the quarter Kelly percentage was 0.1189. The training return was -17.0122 percent, the number of trades was 9, and the expectancy was -1.9607 percent. The out-of-sample return was 3.4468 percent. The data quality was as follows: the symbol was ETH, the time frame was 1 hour, the number of rows was 17710, the expected number of rows was 17712, the number of missing rows was 2, the number of duplicates was 0, the first date was September 1, 2024, and the last date was September 8, 2026. There were no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, and the N1 bounds were degenerate, not population uncertainty. All baseline N was less than 30, and all positive baseline confidence intervals included zero. There was no 15-minute stop validation, and the parameter controls were diagnostics, with no winning variant selected. The full 144 rows of observations were from 17457 to 17544. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline 'ETH 1Day trend-pullback holdout: insufficient independent trades'. This is a note from an automated trading fund manager. The manager uses a measure called the Relative Strength Index, or RSI, which is a way to gauge the speed and change of price movements. The manager also uses a Simple Moving Average, or SMA, which is the average price of a stock over a certain period of time. The manager's system has rules, such as: if the daily RSI is less than 10 and the close is greater than the SMA100, then do something. The system also looks at hourly data. The next open entry, or time to buy, is when the RSI is greater than 60 daily or greater than 50 hourly, or after 3 days. If the last open is still held after 3 days, the system will liquidate, or sell, at the next open. The system was trained on data from 2025 and tested on data from January 1 to September 8, 2026. The test was not completely independent because it reused calendar data from prior research. The system has fees, which are 0.025% to enter and 0.025% to exit. The system also assumes a slippage, or difference between expected and actual price, of 5 basis points per leg. A basis point is one hundredth of a percent. The system's performance is measured by its return, which is -3.758260817716291%. The system's drawdown, or largest loss, is also -3.7582608177162857%. The system has a win percentage of 0% and an average win percentage of 0%. The system's expectancy, or expected return, is -3.7582608177162897%. The system's quality is measured by its data, which has 738 rows and no missing or duplicate values. The data starts on September 1, 2024, and ends on September 8, 2026. The system uses data from two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline about an automated trading fund manager's notes. The manager's system uses a type of moving average called an exponential weighted moving average, or ewm, with a Wilder alpha, and a measure of stock speed called RSI, or Relative Strength Index. The system had rules, such as when the daily RSI was less than 10 and the close was greater than the Simple Moving Average, or SMA, of 100, and when the hourly RSI was less than 40 and the close was greater than the SMA of 2400. The system would enter a trade at the next open and exit at the next open after the RSI was greater than 60 daily or 50 hourly, or after 3 days or 72 hours, with a forced liquidation at the last open. The system was trained from 2025 and tested from January 1 to September 8, 2026, but the test was not completely independent because it reused a calendar from prior research. The system had fees of 0.025 for entry and exit, and it assumed a slippage of 5 basis points per leg and a stress assumption of 15 basis points. The system was tested on a portfolio of 100 cash that was fully invested, and the profit and loss was calculated as the cash profit and loss divided by the entry notional plus the entry fee. The system had a baseline return of 0.627 percent and a drawdown of 3.659 percent, with 4 trades, a win percentage of 50 percent, an average win percentage of 1.889 percent, and an average loss percentage of 1.547 percent. The system also had an expectancy of 0.171 percent and a quarter Kelly criterion of 0.0227. The training return was 2.786 percent, and the out-of-sample return was -0.175 percent. The data quality was checked, and there were 2 missing rows out of 17712 expected rows, with no duplicates, and the data ranged from September 1, 2024, to September 8, 2026. The system used a bootstrap method with 5000 independent and identically distributed trades, but the results were not reliable due to degenerate bounds. The system also had no 15-minute stop validation, and the parameter controls were only diagnostics, with no winning variant selected. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the note that the daily trend for Bitcoin had a pullback holdout that was insufficient due to a lack of independent trades. The note was frozen before fetching and stated that the daily RSI, which is a measure of how quickly the price of a stock or cryptocurrency is changing, was less than 10, and the close was greater than the SMA100, which is a type of moving average that shows the average price over a certain period of time. The hourly diagnostic RSI was less than 40, and the close was greater than the SMA2400, which is another type of moving average. The next open entry would be exited at the next open after the RSI was greater than 60 daily or 50 hourly, or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was from September 2024, and the training period was from 2025. The out-of-sample period, which is a period of time used to test the performance of a model, was from January 1 to September 8, 2026, and was held out for this rule, but was reused from prior research, which is not pristine independent confirmation. The fees for entry and exit were 0.025 and 0.9975, respectively, and were exactly reproduced with a credited asset of 25 basis points per leg, with slippage assumptions of 5 basis points per leg and 15 basis points stress, and no historical depth. The portfolio was isolated with 100 cash and was fully invested. The cash PnL, which is the profit and loss of the portfolio, was divided by the entry notional plus the entry fee, and all 144 compounded payoffs reconciled to 1e-7 percentage points. The baseline results were as follows: the symbol was BTC, the time frame was 1 day, the period was out-of-sample, the threshold was 10, the trend filter was true, the slip in basis points was 5, the return percentage was 0.18177174799714635, the drawdown percentage was 1.8565435856762624, the number of trades was 3, the win percentage was 66.66666666666666, the average win percentage was 0.7451625511902433, the average loss percentage was 1.290625790259734, the expectancy percentage was 0.06656643737358425, the confidence interval low percentage was -1.290625790259734, the confidence interval high percentage was 1.3954415537106202, the quarter Kelly was 0.02233285786680303, and the held missing was 0. The training return was 2.3869241956420666%, the number of trades was 7, the expectancy was 0.36453314982556506%, and the held missing was 0. The stress out-of-sample return was -0.4175198590269105%. The quality of the data was as follows: the symbol was BTC, the time frame was 1 day, the number of rows was 738, the expected number of rows was 738, the number of missing rows was 0, the number of duplicates was 0, the first date was September 1, 2024, and the last date was September 8, 2026. There were no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, and the N1 bounds were degenerate, not population uncertainty. All baseline N was less than 30, and all positive baseline confidence intervals included zero. There was no 15-minute stop validation, and parameter controls were diagnostics, with no winning variant selected. The full 144 rows of observations were from 17457 to 17544. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline about HUBB 2026 ablation_no_sma: frozen 20/5 electrical breakout. The trading strategy, called Preregister1385, used data from 2026 to September 8. It worked as follows: if the stock closed higher than its 50-day Simple Moving Average, which is the average of the last 50 days of trading, then the strategy would hold the stock for 5 sessions and then sell. The strategy did not use the 50-day Simple Moving Average for this test, which is called an ablation. There were costs of 0.1% fee plus 0.1% slippage, which is the difference between the expected price and the actual price, on each side of the trade. The data was adjusted daily using the Securities Information Processor, or SIP. The results were verified using manual compounding from vectorbt. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The results were as follows: return of 7.3594412063739405%, drawdown of 6.444767464595802%, sample size of 9, win rate of 55.55555555555556%, average win of 1.9044055710644336%, average loss of 0.5660111983929988%, and mean of 0.8064425624166859%. The manual return was 7.3594412063738845%. The baseline trade dates and returns were: January 5-12, 2026, January 14-22, 2026, January 30-February 6, 2026, February 10-18, 2026, February 20-27, 2026, April 9-16, 2026, April 21-28, 2026, June 17-25, 2026, and August 5-12, 2026, with returns of -0.0009072605700398695, 0.008819856212863186, 0.00006152074017973241, 0.030373035942439675, -0.016980343310967982, -0.003362906146165745, -0.001389937908546357, 0.04130353571598655, and 0.04078685622110889. The 95% bootstrap mean confidence interval was -0.15619409475090745% to 2.4420381181021766%. There were 9 selected surviving stocks, with no event causal identification and no 15-minute routing. The training rule was frozen and deeply negative, with no approval despite a positive point estimate.

A news scan found the headline 'HUBB 2026 stress: frozen 20/5 electrical breakout'. This refers to a stress test on the stock HUBB in the year 2026, using a specific trading rule. The rule is to hold a stock for 5 sessions after its 20-day moving average, or SMA, which is a calculation of the average price of a stock over 20 days, crosses above its 50-day SMA. The test started on January 5, 2026, and ran until September 8, 2026. The costs of the trades include a fee of 0.1% and slippage of 0.3% on each side of the trade. The results of the test are as follows: the return on investment was 6.23%, the maximum drawdown, or loss, was 5.46%, and the win rate was 44.44%. The average win was 2.62% and the average loss was 0.85%. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The baseline trade dates and returns were from January 5, 2026, to August 12, 2026. The 95% confidence interval for the mean return was between -0.16% and 2.44%. The test was run on 9 selected stocks, with no event causal identification and no 15-minute routing. The training rule was frozen, meaning it was not adjusted during the test, and was deeply negative, meaning it did not perform well. Despite a positive point estimate, the rule was not approved.

A news scan found the headline about HUBB 2026 holdout with a frozen 20/5 electrical breakout. The trading strategy, called Preregister1385, used a chronological approach from 2026 to September 8, with a fixed 20-day close high and a 50-day Simple Moving Average, or SMA, which is a calculation of the average price of a stock over a certain period. The strategy held positions for 5 sessions and exited at the open, with no overlap. The costs included a 0.1% fee plus 0.1% slippage on each side, which refers to the difference between the expected and actual price of a trade. The SIP adjustment, which stands for Securities Information Processor, was applied daily to all stocks. The vectorbt manual compounding, a type of interest calculation, was verified. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The results showed a return percentage of 10.1222%, a drawdown percentage of 5.4564%, a sample size of 9, a win rate of 55.5556%, an average win percentage of 2.4269%, an average loss percentage of 0.5660%, and a mean percentage of 1.0967%. The baseline trade dates and returns were: January 5-12, 2026, January 14-22, 2026, January 30-February 6, 2026, February 10-18, 2026, February 20-27, 2026, April 9-16, 2026, April 21-28, 2026, June 17-25, 2026, and August 5-12, 2026, with returns of -0.0009%, 0.0088%, 0.0001%, 0.0304%, -0.0170%, -0.0034%, -0.0014%, 0.0413%, and 0.0408%, respectively. The 95% bootstrap mean confidence interval was -0.1562% to 2.4420%. The training frozen rule was deeply negative, and there was no approval despite a positive point estimate.

A trading fund manager used a computer program to test a strategy for buying and selling the stock HUBB from 2022 to 2025. The program looked at the last 40 days of stock prices and held each position for 10 days. It bought when the stock closed higher than its highest price in the last 40 days and the 50-day simple moving average, and sold 10 days later. The program did not trade across years and did not overlap trades. It also accounted for a 0.1% fee and 0.1% slippage on each trade. All money was reinvested between trades, with interest compounded on each trade. The data came from a website that provides stock prices. The results showed a return of -12.464642386201467% and a maximum loss of 26.43425113097684%. The strategy won 58.333333333333336% of the time, with an average win of 3.402503830801059% and an average loss of 5.338139378189855%. The average return per trade was -0.2394308396118221%. A news scan found the headline was not applicable here, this is a raw script output. The strategy was tested with a sensitivity analysis, which means it was tested with different parameters to see how it performed, and the results were unchanged despite some adjustments.

A trading strategy was tested on the HUBB stock from 2022 to 2025 with a 40-day lookback period, meaning it considered the last 40 days of data, and a 5-day hold period, meaning it kept each trade for 5 days. The strategy was to enter a trade when the stock's closing price was higher than its prior rolling high and its 50-day simple moving average, and to exit the trade 5 days later. The strategy did not allow overlapping trades or trades that crossed into a new year. A 0.1% fee and 0.1% slippage were applied to each trade. The strategy was tested with all cash between positions, and the returns were compounded, meaning they were added to the initial investment, for each stock, not for the entire account. The data came from a website that provides stock prices. The results showed a return of -28.68%, a drawdown of 29.38%, and a win rate of 46.81%. The average win was 2.61% and the average loss was 3.51%. The strategy was also tested with a 20-day lookback period and a 5-day hold period, but the results were not significantly different. This test was to see how sensitive the strategy was to changes, not to prove its effectiveness.

A training-only period from 2022 to 2025 was used to test the sensitivity of a trading strategy for HUBB with a lookback period of 40 days and a hold period of 3 days. The strategy was to enter a position at the next open if the close was greater than the prior rolling high and the 50-day Simple Moving Average, and to exit at the open 3 days later. No overlapping trades and no cross-year trades were allowed. A fee of 0.1% and slippage of 0.1% were applied to each side of the trade. All cash was reinvested between positions, with compounding applied to the full notional value per name, not account allocation. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The exact metrics from this training period were: a lookback of 40 days, a hold of 3 days, a return of -39.8785%, a drawdown of 40.6248%, 57 samples, a win rate of 35.0877%, an average win of 1.9297%, an average loss of 2.3561%, and a mean of -0.8523%. The manual compounded cash return matched these results. The parameters of 20/5 were unchanged despite ranking, as this is a test of training sensitivity, not holdout evidence.

A training-only period from 2022 to 2025 was used to test the sensitivity of the HUBB training with a lookback period of 20 days and a hold period of 10 days. The trading rules were to enter a position at the next open if the close was higher than the prior rolling high and the 50-day Simple Moving Average, and to exit the position at the open 10 days later. No overlapping trades and no trades that crossed years were allowed. The Securities Information Processor adjustment was applied to all trades, and a 0.1% fee and 0.1% slippage were applied to each side of the trade. All cash was reinvested between positions, with compounding applied to the full notional value per name, but not to the account allocation. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The exact metrics were: lookback 20, hold 10, return percentage 0.21514402459439225, drawdown percentage 22.189756482393484, sample size 41, win rate percentage 60.97560975609756, average win percentage 3.750598094495841, average loss percentage 5.449989033369523, mean percentage 0.1601250689874067, and manual return percentage 0.21514402459432702. The manual compounded cash matched the results. The Frozen20/5 ranking remained unchanged, but this is a test of training sensitivity, not evidence from a holdout set.