A news scan found the analysis of QVM90806 SPY unscaled managed exits. The analysis used data from 2016 to 2018 for calibration, 2019 to 2020 for training, 2021 to 2025 as a holdout period, and recent data from 2026 up to September 4. The strategy used the first monthly session and skipped overlapping sessions. It used a prior ATR, or Average True Range, which is a measure of volatility, of 14 as a stop loss at 1 ATR and a target at 1.5 ATR, with half of the position closed at a 1R return, then moving to a break-even point. The same bar or gap ordering was used for adverse conditions. Fees and slippage were set at zero, base, and stress levels of 0, 5, and 20 basis points per fill. The analysis used fractional diagnostics, a cash reserve of 1 part per million, and adjusted OHLC, or open, high, low, and close prices, not including cash dividends or 15-minute intervals. Every fill and daily cash ledger matched the vectorbt output exactly after draft sign correction. The win, loss, and return on invested amount were measured, not the weighted book return, and sizing also needed a weighted performance gate. The results were as follows: nine cells showed returns ranging from 30.53% to -24.59%, with win rates from 66.67% to 41.67%. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, which is a website providing stock bar data. All 162 cells of individually recorded QVM90806 variant observations were used, with no out-of-sample selection.
The automated trading fund manager analyzed the QVM90806 SPY, which is a type of investment strategy. The analysis used data from 2016 to 2018 for calibration, 2019 to 2020 for training, 2021 to 2025 as a holdout period, and recent data from 2026 up to September 4. The strategy looked at the first session of each month and skipped any overlapping sessions. It exited trades at the close of the 20th session and did not require venue exits, which are exits based on specific market conditions. The analysis considered three types of fees: zero, base, and stress, with slippage per fill of 0, 5, and 20 basis points, which are units of measurement for interest rates or fees. The diagnostic used a fractional approach, with a cash reserve of 1 part per million, and adjusted for opening, high, low, and closing prices, but not for cash dividends or 15-minute intervals. Every fill and daily cash ledger matched the vectorbt output exactly after a draft sign correction. The win, loss, and return on invested amount were measured, not the weighted book return, which is a measure of investment return that takes into account the size of the investment. The sizing also needed a weighted performance gate, which is a threshold for evaluating performance. The analysis looked at nine cells, or periods, with different costs and returns. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, which is a website that provides stock market data. All 162 cells, or variant observations, were individually recorded for the QVM90806 variant, and there was no out-of-sample selection, which means that the data used for testing was not used for training.
This is a record of a trading strategy, which is not optimized, called Frozen strategy926. The strategy was trained on data from 2016 to 2020, held out from 2021 to 2025, and recently used from 2026 through September 4. The costs considered are zero, base, and stress costs. The results are reported for the holdout period only. The full results are as follows: there are 9 sets of data across different periods and costs. A news scan found the data line with the following figures: for the train period with zero cost, the return was 19.20315865283131, the maximum drawdown was 33.40625556193522, and the win percentage was 51.64319248826291. For the holdout period with zero cost, the return was -32.59505817592977, the maximum drawdown was 41.18284700195467, and the win percentage was 45.58139534883721. For the recent period with zero cost, the return was -0.6619567187630349, the maximum drawdown was 26.80240913305211, and the win percentage was 51.724137931034484. The same metrics are reported for the base and stress costs. The strategy uses a liquid 12 country ETF with a weekly negative worst return, and it considers the prior close signal and the next open. It uses one position and skips overlaps. The stop loss is set at 1 ATR, the target is set at 1.5 ATR, and the half plus 1 R then BE, with adverse same bar ordering. The model is fractional, and fees and slippage are explicitly considered. The data is from https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics are separately recorded for the QREV90804 variant 001, with 240 observations, and there is no family winner selection.
This is a record of a trading strategy, which is not optimized, from the Frozen strategy 926 notes 920. The record covers 9 cells across different time periods: training from 2016 to 2020, holdout from 2021 to 2025, and recent from 2026 through September 4. The costs considered are zero, base, and stress costs. The figures reported are for the baseline holdout only. The full data is as follows: there are three periods - train, holdout, and recent - each with different costs - zero, base, and stress. For each period and cost, the return, drawdown, number of trades, win percentage, W, L, and K values are reported. The strategy uses a liquid 12 country ETF with a weekly negative worst return, prior close signal, next open, one position, and skips overlaps. The time exit is only considered, and the strategy is not venue-ready. The model is fractional, with fees and slippage explicitly vectorized and independent cash reconciliation. The chronological holdout was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics are separately recorded for the QREV90804 variant 001, with 240 observations, and there is no family winner selection.
This is a record of the QREV90804 strategy, which is not optimized. The strategy was trained on data from 2016 to 2020, held out from 2021 to 2025, and recently used from 2026 through September 4. The costs considered are zero, base, and stress costs. The figures reported are for the baseline holdout period only. The full results are as follows: there are nine cells across three periods with different costs. The periods are training, holdout, and recent, with costs of zero, base, and stress. The results include return, drawdown, number of trades, win percentage, and other metrics. The strategy uses a liquid 12-country ETF with a weekly negative worst return. It also uses a prior close signal, next open, one position, and skips overlaps. The stop loss is set at 1 ATR, target at 1.5 ATR, and half plus 1 R then break even. The model is fractional, with fees and slippage explicitly vectorized and independent cash reconciliation. The holdout period was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 001 to 240 observations, and no family winner selection was used.
This is a record of the QREV90804 strategy, which is not optimized. The strategy was tested on data from 2016 to 2020, and its performance was evaluated on data from 2021 to 2025. The results are reported for the holdout period only. The full results are as follows: the strategy had a return of 57.84436043079979% during the training period, 125.27921857208568% during the holdout period, and 5.390037197775399% during the recent period, with corresponding drawdowns of 35.49557826654425%, 25.253188262610806%, and 27.31440360804992%. The strategy also had win rates of 50.442477876106196%, 57.07964601769911%, and 58.620689655172406% during the respective periods. A news scan found the headline is not applicable here, this is a data analysis. The strategy used a cost of zero, base, and stress, which refers to a high level of risk. The results show that the strategy performed best with a cost of zero, with a return of 125.27921857208568% during the holdout period. The strategy used a liquid 12 country ETF with a weekly negative worst return, and the signal was based on the prior close and next open. The strategy had one position and skipped overlaps. The time exit was only used, and the strategy was not venue-ready. The model used was fractional, with fees and slippage explicitly vectorized and independent cash reconciliation. The chronological holdout was previously inspected for other strategies, and daily OHLC and adjustments were not validated at 15-minute intervals or for cash dividends. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant 001 to 240 observations, with no family winner selection.
This is a record of a trading strategy, which is not optimized, called Frozen strategy926. The strategy was trained on data from 2016 to 2020, held out from 2021 to 2025, and recently used until September 4, 2026. The costs considered are zero, base, and stress costs. The results are reported for the holdout period only. The full results are: for the training period with zero costs, the return was 52.227473811057855, the maximum drawdown was 34.93389636286501, and the win rate was 54.95495495495496; for the holdout period with zero costs, the return was -21.182971924873883, the maximum drawdown was 37.43068337612203, and the win rate was 51.4018691588785; for the recent period with zero costs, the return was -2.78327190421227, the maximum drawdown was 25.13463563534557, and the win rate was 54.83870967741935; for the training period with base costs, the return was -2.351343063306033, the maximum drawdown was 47.64123339705238, and the win rate was 52.702702702702695; for the holdout period with base costs, the return was -48.626163252452095, the maximum drawdown was 53.41515686636533, and the win rate was 50.467289719626166; for the recent period with base costs, the return was -8.627661425036425, the maximum drawdown was 27.637272325848205, and the win rate was 51.61290322580645; for the training period with stress costs, the return was -49.83213221076598, the maximum drawdown was 64.83902029350193, and the win rate was 48.64864864864865; for the holdout period with stress costs, the return was -72.96511939062655, the maximum drawdown was 73.57239554301023, and the win rate was 47.66355140186916; for the recent period with stress costs, the return was -16.74213253388731, the maximum drawdown was 31.235249486976446, and the win rate was 48.38709677419355. The strategy uses a liquid 12-country ETF with a weekly negative worst return, prior close signal, next open, one position, and skips overlaps. The stop loss is set at 1 ATR, target at 1.5 ATR, and half plus 1R then break even, with adverse same bar ordering. The model is fractional, with fees and slippage explicitly vectorized and independent cash reconciliation. The chronological holdout was previously inspected for other strategies, with daily OHLC and adjustments, not 15-minute cash dividend validation. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 001 to 240 observations, and no family winner selection.
This is a record of a trading strategy, which has not been optimized. The strategy was tested on data from 2016 to 2020, and its performance was evaluated on data from 2021 to 2025, as well as recent data up to September 4, 2026. The results are reported for the holdout period only. A holdout period is a time frame used to test a strategy. The full results are: a list of performance metrics for different time periods and cost scenarios, including return, drawdown, and win rate. The strategy uses a fractional model, which means it can buy and sell fractions of a stock, and it takes into account fees and slippage, which is the difference between the expected price of a trade and the actual price. The data used to test the strategy comes from a source that provides daily high, low, open, and close prices, as well as adjustments for cash dividends. The strategy was tested on a dataset of 240 observations, and no winner selection was used, meaning that no attempt was made to select the best performing strategy from a group of strategies. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.
This is a record of a trading strategy, which is not optimized, from a frozen strategy926/notes920. The record covers 9 cells across different time periods: training from 2016 to 2020, holdout from 2021 to 2025, and recent from 2026 through September 4. The costs considered are zero, base, and stress. The figures reported are for the baseline holdout only. The full data is as follows: there are three periods - train, holdout, and recent - each with different costs - zero, base, and stress - and corresponding returns, drawdowns, and other metrics. A news scan found the data line with the following details: a list of periods with their respective costs, returns, drawdowns, number of trades, win rates, and other metrics. The strategy uses a liquid 12 country ETF with a weekly negative worst return, prior close signal, and next open, with one position and no overlaps. The stop loss is set at 1 ATR, target at 1.5 ATR, and half plus 1 R then break even, with adverse same bar ordering. The model is fractional, with explicit fees and slippage vectorbt and independent cash reconciliation. The chronological holdout was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics are separately recorded for the QREV90804 variant 001, with 240 observations and no family winner selection.
This is a record of a trading strategy, which is not optimized. The strategy was tested on 9 cells of data from 2016 to 2020, and then applied to data from 2021 to 2025, and from 2026 up to September 4. The costs were considered as zero, base, and stress. The results are reported for the holdout period only. The full results are: for the training period with zero costs, the return was 178.1938990668753, the maximum drawdown was 16.39947720096234, and the win rate was 55.41125541125541; for the holdout period with zero costs, the return was 293.19855280009165, the maximum drawdown was 17.430076245682258, and the win rate was 60.36036036036037; for the recent period with zero costs, the return was -3.5964203195659183, the maximum drawdown was 24.204642329198002, and the win rate was 61.29032258064516; for the training period with base costs, the return was 75.2683644998524, the maximum drawdown was 24.664730330157692, and the win rate was 54.54545454545454; for the holdout period with base costs, the return was 152.2232658083544, the maximum drawdown was 22.351464394474295, and the win rate was 57.20720720720721; for the recent period with base costs, the return was -9.391925701100046, the maximum drawdown was 25.823788821665705, and the win rate was 51.61290322580645; for the training period with stress costs, the return was -12.353025047036681, the maximum drawdown was 47.25899957864242, and the win rate was 48.05194805194805; for the holdout period with stress costs, the return was 29.581950734311448, the maximum drawdown was 33.228683495225894, and the win rate was 47.2972972972973; for the recent period with stress costs, the return was -17.438525061406708, the maximum drawdown was 28.52340307978135, and the win rate was 38.70967741935484. The strategy used a weekly negative worst return signal from a 12-country ETF, and it only exited positions at the end of the time period, not at the venue. The model used was fractional, with explicit fees and slippage, and independent cash reconciliation. The holdout period was previously inspected for other strategies, and the data used was daily OHLC and adjustments, not 15-minute cash dividend validation. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 001 to 240 observations, and no family winner selection.
This is a record of a trading strategy, which is not optimized, called Frozen strategy926. The strategy was trained using data from 2016 to 2020, tested on data from 2021 to 2025, and recently used on data from 2026 up to September 4. The costs considered are zero, base, and stress costs. The results are reported for the holdout period only. The full results are: a list of performance metrics for different periods and costs, including return, drawdown, number of trades, win percentage, and other metrics. The strategy uses a liquid 12-country ETF with a weekly negative worst return. It uses a prior close signal and next open signal, with one position and no overlaps. The strategy also uses a stop-loss and target system, with a fractional model and explicit fees and slippage. The data used is from a source called runway, and all metrics are separately recorded for the QREV90804 variant, with 240 observations and no family winner selection.
This is a record of a trading strategy, which is not optimized, called QREV90804. The strategy was tested on data from 2016 to 2020, and its performance was evaluated on data from 2021 to 2025, and from 2026 up to September 4. The costs considered were zero, base, and stress costs. The results are reported for the holdout period only. The full results are as follows: there were nine sets of data, each with its own costs and returns. For example, during the training period with zero costs, the return was 37.43%, the maximum drawdown was 31.10%, and the win rate was 50.26%. The strategy used a weekly negative worst return signal from a 12-country ETF, and it only exited positions at the end of the trading day. The model used was fractional, and fees and slippage were explicitly considered. The data used was from the website https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were recorded separately for the QREV90804 variant, with 240 observations, and no family winner selection was used.
A news scan found the headline 'QREV90804 sweep intraday lookback3 hold3 managed'. This is a record of a trading strategy, which is not optimized, called Frozen strategy926/notes920. The strategy was tested on 9 cells of data from 2016 to 2020, and the results from 2021 to 2025 were used to evaluate its performance, with the most recent data being from 2026 up to September 4. The costs considered were zero, base, and stress costs. The results are as follows: the strategy had a return of 32.31424135822174% during the training period, a return of -4.382806977449949% during the holdout period, and a return of -4.528947283584362% during the recent period, all with zero costs. With base costs, the returns were -15.801283969165347%, -38.54208630517213%, and -10.088749017283282% for the training, holdout, and recent periods, respectively. With stress costs, the returns were -57.25815206631386%, -68.33058305254215%, and -17.827316762377265% for the training, holdout, and recent periods, respectively. The strategy used a liquid 12 country ETF with a weekly negative worst return, and it considered the prior close signal and the next open signal. It also used a stop loss of 1 ATR, a target of 1.5 ATR, and a half plus 1 R then BE. The model was fractional, and fees and slippage were explicitly considered. The data used was from https://runway.adityaperswal.workers.dev/data/stock-bars, and all metrics were separately recorded for the QREV90804 variant 001, with 240 observations and no family winner selection.
A news scan found the headline 'Frozen strategy926/notes920, not optimized.' This record groups 9 cells across training from 2016 to 2020, held out from 2021 to 2025, and recent data from 2026 through September 4, considering zero, base, and stress costs. Figures report baseline holdout only. The full data is as follows: there are nine periods with different costs and returns. The periods are train, holdout, and recent, each with zero, base, and stress costs. For example, the train period with zero cost had a return of 109.22542474738881, a drawdown of 23.449315758242694, 226 trades, a win rate of 53.53982300884957, and other metrics. The data also includes a liquid 12 country ETF with weekly negative worst return, prior close signal, next open, one position, and skip overlaps. Time exit only, not venue-ready, with a fractional model and fees and slippage as an explicit vector. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, with 240 observations and no family winner selection.
A frozen strategy, which means it is not optimized, was used to manage a trading fund. This strategy was applied to 9 cells of data from 2016 to 2020 for training, 2021 to 2025 for holdout, and 2026 up to September 4 for recent data. The costs considered were zero, base, and stress costs. The results are reported for the holdout period only. The full results are as follows: there were three periods - train, holdout, and recent - each with different costs - zero, base, and stress. For each period and cost, the return, drawdown, number of trades, win percentage, W, L, and K values were calculated. The liquidation strategy used a 12-country ETF with a weekly negative worst return. The prior close signal was used to determine the next open position, with one position at a time and no overlaps. The stop loss was set at 1 ATR, the target at 1.5 ATR, and half plus 1 R then break even. The model used was fractional, with fees and slippage explicitly vectorized and independent cash reconciliation. The holdout data was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 001 to 240 observations and no family winner selection.
A news scan found the headline 'Frozen strategy926/notes920, not optimized.' This record groups 9 cells across training from 2016 to 2020, held out from 2021 to 2025, and recent from 2026 through September 4, with zero, base, and stress costs. Figures report baseline holdout only. The full rows are: a list of performance metrics for different periods and costs, including return, drawdown, number of trades, win percentage, and other statistics, such as W, L, and K, which are measures of trading performance. The strategy uses a liquid 12 country ETF with a weekly negative worst return, prior close signal, next open, one position, and skips overlaps. Time exit only, not venue-ready. The model is fractional, with fees and slippage, which is a measure of the difference between expected and actual trading costs, explicitly vectorized and with independent cash reconciliation. The chronological holdout was previously inspected for other strategies, with daily OHLC, or open, high, low, and close, and adjustments, but not 15 minute or cash dividend validation. The source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics are separately recorded for QREV90804 variant 001, with 240 observations, and no family winner selection.
This is a record of a trading strategy, which is not optimized. The strategy was trained using data from 2016 to 2020, and its performance was tested on data from 2021 to 2025 and from 2026 up to September 4. The costs were considered to be zero, base, or stress. The results are reported for the holdout period only. The full results are as follows: there were three periods - train, holdout, and recent - and three cost types - zero, base, and stress. For each period and cost type, the return, drawdown, number of trades, win percentage, and other metrics were calculated. The strategy used a liquid 12-country ETF with a weekly negative worst return. It also used a prior close signal and next open position, with no overlaps. The strategy had a stop loss of 1 ATR, a target of 1.5 ATR, and a risk-reward ratio of 1:1.5. The model was fractional, with explicit fees and slippage, and independent cash reconciliation. The holdout period was previously inspected for other strategies, and the data used was daily OHLC and adjustments, not 15-minute or cash dividend validation. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 240 observations and no family winner selection.
This is a record of a trading strategy, which is not optimized, from a frozen strategy926/notes920. The record covers 9 cells across different time periods: training from 2016 to 2020, holdout from 2021 to 2025, and recent from 2026 through September 4. The costs considered are zero, base, and stress costs. The figures reported are for the baseline holdout only. The full rows of data are provided in a list, which includes the period, cost, return, drawdown, number of trades, win percentage, and other metrics. The list includes data for the train, holdout, and recent periods with zero, base, and stress costs. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a liquid 12 country ETF with a weekly negative worst return, prior close signal, next open, one position, and skips overlaps. The time exit is only considered, and it is not venue-ready. The model is fractional, with explicit fees and slippage vectorbt and independent cash reconciliation. The chronological holdout was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. All metrics are separately recorded for the QREV90804 variant 001, with 240 observations, and no family winner selection.
This is a record of a trading strategy, which is not optimized. The strategy was trained on data from 2016 to 2020, held out from 2021 to 2025, and recently used from 2026 through September 4. The costs considered are zero, base, and stress costs. The results are reported for the holdout period only. The full results are: a list of performance metrics for different periods and costs, including return, drawdown, number of trades, win percentage, and other metrics. The strategy uses a fractional model, with fees and slippage explicitly considered. The model also uses a stop-loss and target system, with a prior close signal and next open position. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics are separately recorded for the QREV90804 variant, with 240 observations and no family winner selection.
A news scan found the notes of an automated trading fund manager. The strategy, labeled as 926, has notes from 920, but it has not been optimized. This record combines data from 9 cells across different time periods: training from 2016 to 2020, held out from 2021 to 2025, and recent data from 2026 up to September 4. The costs considered are zero, base, and stress costs. The figures reported are for the baseline holdout only. The full data is as follows: there are three periods - train, holdout, and recent - each with different costs - zero, base, and stress. The data includes return, drawdown, number of trades, win percentage, and other metrics. A list of data points shows the performance of the strategy across these periods and costs. The strategy uses a fractional model, with fees and slippage considered, and independent cash reconciliation. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were recorded separately for the QREV90804 variant, with 001 to 240 observations, and no family winner selection was made.
The QREV90804 strategy was not optimized. This record combines 9 cells across different time periods: training from 2016 to 2020, holdout from 2021 to 2025, and recent from 2026 through September 4. The costs considered are zero, base, and stress. The figures reported are for the baseline holdout only. The full data is as follows: there are three periods - train, holdout, and recent - each with different costs - zero, base, and stress. For each period and cost, the return, drawdown, number of trades, win percentage, and other metrics are reported. The strategy uses a liquid 12 country ETF with a weekly negative worst return. It also uses a prior close signal and next open, with one position and no overlaps. The stop loss is set at 1 ATR and the target is set at 1.5 ATR. The model is fractional and considers fees and slippage. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics are separately recorded for the QREV90804 variant, with 001 to 240 observations and no family winner selection.
This is a record of a trading strategy, which is not optimized, from a frozen strategy926/notes920. The strategy was trained from 2016 to 2020, held out from 2021 to 2025, and recently used from 2026 through September 4. The costs considered are zero, base, and stress costs. The results are reported for the holdout period only. The full results are as follows: there are nine cells across three periods - training, holdout, and recent - with different costs. The figures include return, drawdown, number of trades, win percentage, and other metrics. The strategy uses a liquid 12 country ETF with a weekly negative worst return, prior close signal, and next open, with one position and skipping overlaps. The strategy only considers time exits and is not venue-ready. It uses a fractional model with explicit fees and slippage, and independent cash reconciliation. The holdout period was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 001 to 240 observations, and no family winner selection was used.
The QREV90804 strategy was not optimized. This record combines 9 cells across training from 2016 to 2020, held out from 2021 to 2025, and recent data from 2026 through September 4, with zero, base, and stress costs. The figures only report the baseline holdout. The full data is as follows: there are three periods, train, holdout, and recent, each with different costs, returns, and other metrics. The data includes the return, which is the profit or loss, the drawdown, which is the largest decline from a peak, the number of trades, the win rate, and other metrics. The strategy uses a liquid 12 country ETF with a weekly negative worst return, and it skips overlaps. It also uses a prior close signal and next open, with one position. The stop loss is set at 1 ATR, the target is set at 1.5 ATR, and half plus 1 R then break even, with adverse same bar ordering. The model is fractional, with fees and slippage explicitly vectorized, and independent cash reconciliation. The chronological holdout was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 001 to 240 observations, and no family winner selection.
This is a record of a trading strategy, specifically the QREV90804 sweep total lookback 3 hold 5 parent, which has not been optimized. The strategy was tested on data from 2016 to 2020, and its performance was evaluated on data from 2021 to 2025, as well as recent data up to September 4, 2026. The evaluation considered zero, base, and stress costs. The results are reported for the holdout period only. The full results are as follows: there are nine cells across three periods - training, holdout, and recent - with different costs. A news scan found the data line with the following figures: for the training period with zero cost, the return was 48.77545160177237, the maximum drawdown was 39.29767978388606, and the win rate was 55.44041450777202. For the holdout period with zero cost, the return was 44.398438556889985, the maximum drawdown was 29.820176916222042, and the win rate was 49.23076923076923. For the recent period with zero cost, the return was -6.05867915929772, the maximum drawdown was 34.34440185422142, and the win rate was 46.42857142857143. Similar figures are reported for base and stress costs. The strategy used a liquid 12 country ETF with weekly negative worst return, prior close signal, and next open, with one position and skipping overlaps. The time exit was only considered, and the strategy was not venue-ready. The model used was fractional, with explicit vector backtest and independent cash reconciliation. The chronological holdout was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant 001, with 240 observations, and no family winner selection was made. Drawdown, or maximum drawdown, refers to the largest decline in value from a peak to a trough. Win rate refers to the percentage of winning trades.
This is a record of a trading strategy, which is not optimized, from Frozen strategy 926 and notes 920. The record covers three periods: training from 2016 to 2020, holdout from 2021 to 2025, and recent from 2026 to September 4. The costs considered are zero, base, and stress. The report only shows figures for the baseline holdout period. The full data is as follows: there are nine cells across the training, holdout, and recent periods. A news scan found the headline is not applicable here, this is a data line. The data line reports the following metrics: return, drawdown, number of trades, win percentage, W, L, and K. For example, in the training period with zero costs, the return was 57.561133899861574, the drawdown was 29.861548652970704, and the win percentage was 56.30630630630631. The strategy uses a liquid 12 country ETF with a weekly negative worst return. It also uses a prior close signal and next open, with one position and skipping overlaps. The stop loss is set at 1 ATR, the target is 1.5 ATR, and half plus 1 R then break even. The model is fractional, with fees and slippage explicitly vectorized and independent cash reconciliation. The holdout period was previously inspected for other strategies, with daily OHLC and adjustments, but not 15-minute cash dividend validation. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. All metrics were separately recorded for the QREV90804 variant, with 001 to 240 observations, and no family winner selection.