The notes are from an analysis of the QVM90806 EFA, which is an automated trading fund. The analysis was done in three periods: 2016-2018 for calibration, 2019-2020 for training, 2021-2025 for holdout, and 2026 through September 4 for recent data. The analysis looks at the performance of the fund on a monthly basis, starting from the first session of each month and skipping any overlapping sessions. The fund's performance is measured by its return on invested amount, not by its weighted book return. The analysis also considers fees and slippage, which are costs associated with buying and selling, and are set at 0, 5, and 20 basis points for zero, base, and stress fees respectively. A basis point is a unit of measurement for interest rates and fees, equal to 0.01%. The analysis uses a diagnostic tool to evaluate the fund's performance, and it also keeps a cash reserve of 1 part per million. The results of the analysis are shown in nine cells, each representing a different period and cost scenario. The cells show the return, drawdown, number of sessions, win rate, and other performance metrics for each scenario. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All 162 cells of data were individually recorded for the QVM90806 variant, and there was no out-of-sample selection, meaning that the data used for the analysis was the same as the data used for the training and testing of the fund.
A news scan found the analysis of QVM90806, an automated trading fund manager. 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 manager made decisions based on the first monthly session and skipped overlapping periods. It used a stop-loss order, which is an order to sell a security when it reaches a certain price, set at 1 ATR, or average true range, a measure of volatility. The target was set at 1.5 ATR. The manager also used a risk management strategy called adverse same bar or gap ordering. Fees and slippage, which is the difference between the expected price of a trade and the actual price, were set at zero, base, and stress levels, with slippage at 0, 5, and 20 basis points. The analysis included fractional diagnostic, cash reserve, 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 performance was measured by return on invested amount, not weighted book return. The analysis also considered sizing and weighted performance gate. The results are shown in nine cells, each representing a different period and cost scenario. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All 162 cells of individually recorded QVM90806 variant observations were used, with no out-of-sample selection.
The analysis of QVM90806, an inverse variance strategy on the SPY stock, was based on data from 2016 to 2026. The data was split into training, holdout, and recent periods. The training period was from 2019 to 2020, the holdout period was from 2021 to 2025, and the recent period was from 2026 up to September 4. The strategy was evaluated on a monthly basis, with the first session of each month being the next open, and any overlapping sessions were skipped. The time exit was set at the close of the 20th session, and venue exits were not required. The fees and slippage were set at 0, 5, and 20 basis points for the zero, base, and stress scenarios, respectively. A diagnostic was run on the data, with a cash reserve of 1 part per million, and the open, high, low, and close prices were adjusted to exclude cash dividends and 15-minute intervals. The results were matched exactly with the vectorbt output after a draft sign correction. The performance was measured by the return on invested amount, not the weighted book return, and the sizing also needed a weighted performance gate. The results are shown in the following nine cells: there were three periods, train, holdout, and recent, each with three cost scenarios, zero, base, and stress. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and all 162 cells of individually recorded QVM90806 variant observations were used, with no out-of-sample selection.
A news scan found the analysis of QVM90806, an automated trading fund manager. 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 manager made decisions based on the first monthly session and skipped overlapping periods. The prior ATR, or Average True Range, which is a measure of volatility, was used to set a stop at 1 ATR and a target at 1.5 ATR, with half of the position closed at a one-to-one risk-reward ratio and the rest at break-even. The same bar or gap ordering was used for adverse conditions. Fees and slippage, which is the difference between expected and actual trade prices, were set at zero, base, and stress levels, with slippage at 0, 5, and 20 basis points per fill. A fractional diagnostic was used, with a cash reserve of 1 part per million, and adjusted OHLC, or open, high, low, and close prices, that did not account 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, and sizing also required a weighted performance gate. The results are shown in nine cells, which are: a list of performance metrics for different periods and cost scenarios, including return, drawdown, number of trades, win rate, and other metrics. 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 QVM90806 variant observations were individually recorded, with no out-of-sample selection.
The analysis of QVM90806, which is an automated trading fund manager, was based on data from 2016 to 2026. The data was split into three periods: calibration from 2016 to 2018, training from 2019 to 2020, holdout from 2021 to 2025, and recent data from 2026 up to September 4. The analysis was done on a monthly basis, with the first session of each month being the next open, and overlapping sessions were skipped. The time exit was set at the 20th session close, and venue exits were not required. The fees and slippage were set at zero, base, and stress levels, which refer to different levels of costs, with zero being no costs, base being normal costs, and stress being high costs. The analysis also included a diagnostic test, which is a test to evaluate the performance of the trading strategy, and a cash reserve of 1 part per million, which is a small amount of cash set aside. The adjusted OHLC, which stands for Open, High, Low, and Close, did not include cash dividends or 15-minute intervals. The results of the analysis were matched exactly with the vectorbt results after a draft sign correction, which is a correction made to the trading signals. The performance was measured by the return on invested amount, not the weighted book return, which is a measure of the return on investment. The sizing also needed a weighted performance gate, which is a measure of the performance of the trading strategy. The results are shown in nine cells, which are: train period with zero cost, train period with base cost, train period with stress cost, holdout period with zero cost, holdout period with base cost, holdout period with stress cost, recent period with zero cost, recent period with base cost, and recent period with stress cost. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. All 162 cells of individually recorded QVM90806 variant observations were used, and there was no out-of-sample selection, which means that the data used for testing was not used for training.
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.
The QVM90806 economic exposure and cost bridge for volatility timing is based on several key points. Firstly, the SPY issuer factsheet describes an S&P 500 price/yield tracker with an annual net expense of 0.0945% and a January 1993 inception. The EFA issuer describes a developed large/midcap index outside of the US and Canada, while the EEM issuer describes an emerging large/midcap index. Broad index earnings and dividends underpin long returns, and inverse variance scaling changes exposure without creating new operating cash flow. Secondly, ETF expenses are already reflected in prices and are not added twice. A cash yield of 0 is assumed, and the total return adjusted OHLC proxy differs from paper excess returns and actual cash dividend timing. Thirdly, foreign equity prices incorporate local cash flows and FX, as well as non-US close timing, meaning that lower USD variance alone cannot identify valuation or credit risk. The binding constraint is the net implementable edge, which is approximately $213 USD, and actual whole-share exits must fit within the available capital. No issuer valuation forecast is used. For example, holding a long-term premium may outperform timing when volatility falls after a rally, and a risk reduction is not necessarily a return improvement. Sources for this information include the SPY, EFA, and EEM issuer factsheets, which can be found at https://www.ssga.com/library-content/products/factsheets/etfs/us/factsheet-us-en-spy.pdf, https://www.ishares.com/us/literature/fact-sheet/efa-ishares-msci-eafe-etf-fund-fact-sheet-en-us.pdf, and https://www.ishares.com/us/products/239637/ishares-msci-emerging-markets-etf.
The QVM90806 volatility management strategy is based on three premises. First, the original rule by Moreira and Muir in 2017 uses a measure called inverse lagged realized variance on factor excess returns, which is a way to calculate volatility. Second, a study by Cederburg et al in 2020 found that there is a difference between how well a strategy performs in theory and how well it performs in real-life trading, after testing 103 different strategies. Third, the trading venue requires a limited amount of cash and explicit exit rules, but neither of the two studies validated this combination. The conclusion is to test the adaptation of the strategy, rather than claiming it can be exactly replicated or that it can produce tradable alpha, which refers to the ability to generate excess returns. The strategy was calibrated using data from 2016 to 2018, trained on data from 2019 to 2020, and tested on data from 2021 to 2025, with a focus on recent data from 2026. The weight of the strategy is determined by the minimum of 1 and a calculation based on the prior 22-day mean squared log return, with a 66-day sensitivity and unscaled control. The strategy also includes a parent 20-session time exit, a managed 1 ATR stop, a 1.5 ATR target, and a half plus 1 ATR then break-even rule, with adverse ordering and non-overlap. The strategy was tested on 162 cells without any tuning. For more details, see notes 965 and 964. The validity of the strategy depends on the distinction between historical factor results and the executable rule, and its soundness depends on the tested price and execution assumptions. The same historical holdout was used in previous strategy research and is not pristine prospective evidence. The sources for this information are https://amoreira2.github.io/alan-moreira.github.io/VolPortfolios_published.pdf and https://www.lehigh.edu/~xuy219/research/COWY.pdf.
The QREV90804 final decision was to reject the country ETF reversal swing and retain quant cash. The process started at 04:00:30 UTC and was completed in approximately 21 minutes, which is under the planned 42 minutes and the hard limit of 45 minutes. The final journal phase began at 04:18. The fund notes, observations, analyses, and lessons were read first, and actual exit, non-overlap, and no funding pressure lessons were applied. A fresh ledger showed 3 closed PEAD operator liquidations with 0 open, pending, or closing, and the quant cash was $245.69. The primary papers were read and the boundary was recorded at 12338/12339, with the third research abstract at 12348 and the issuer structure at 929. The frozen protocol was at 926, with notes at 920 and 924, and 14 ETF panels with 37576 bars from 2016 to 2026 were complete, with a request for pre-2016 missing data. There were 216 main plus 24 full-day skip cells, with all 240 metrics recorded observations and 72 backtest records covering every family. The main managed 36 out of 36 holdout negative, including 12 zero-cost. The primary train from 2016 to 2020 was -8.879916%, the holdout from 2021 to 2025 was -45.074220%, the drawdown was 48.485178%, the number of trades was 216, the win percentage was 48.148148%, the average win was 1.700601%, the average loss was 2.068431%, and the Kelly criterion was 0. The stress test was -71.268856% and the recent test was -22.895816%. Contrary evidence was preserved, with the parent time exit baseline holdout at +41.613514% but the stress test at -20.393113%, and the weekly mean CI spans 0 and matched EFA comparison weak. The recent lag 2 intraday managed +21.969978% with 29 trades does not rescue historical failure. There was no timing or parameter selection after results. The statistical, technical, simulation, and consequences premises were recorded, with uncertainty, matched markets, regimes, Monte Carlo, and binding constraint. The daily bar paths, fractional partial exits, adjusted dividend prices, and assumed fees are not verified for 15-minute venue execution, and the fresh pristine out-of-sample data is missing. The code cash ledger reconciliation proves arithmetic consistency only, with a maximum of 1.0914e-11 USD. The exact readback archive was 13290, with 310 EWG parts, 604064 characters, 37576 input bars, and 6 scripts and 13 files, and all result rows and indicators were preserved, with ledgers regenerable. Lessons 352-355 and handoff 946 were written. There are 8 active jobs out of 15 and 68, with the next job at 04:31 and verification of actual output at 06 UTC. Six quant monitors are future, with none before 06 UTC, and the next market entry would need fresh validation. No additional monitor or order is needed for the rejected rule. The conclusion is that there are no proposals, with an actual size of 0. The sources used include https://runway.adityaperswal.workers.dev/data/stock-bars, https://repub.eur.nl/pub/25718/AnotherLook_2011.pdf, https://www.aqr.com/Insights/Research/Working-Paper/Trading-Costs-of-Asset-Pricing-Anomalies, and https://www.ishares.com/us/products/239665/ishares-msci-japan-etf.
The automated trading fund started with $245.69 in cash and $850.99 in the account, with no open or queued orders. The goal is to have a repeatable and positive expected net payoff. A constraint is that there is no funding gap of $349.01. There are four premises: 1. The primary managed fund lost 8.879916% in training, 45.074220% in holdout, and 22.895816% in 2026, with all 36 managed holdout cells being negative, including 12 with zero cost. 2. The parent baseline had a 41.613514% holdout, but stress testing showed a -20.393113% result, and other tests were inconclusive. 3. A diagnostic test did not improve holdout results, but a counterexample of a 21.969978% gain in 2026 was found. 4. It is not possible to calibrate the probability of a reversal. The conclusion is that no order logically follows the required validation gates. The strongest counterargument is that holding cash means missing out on rallies, with a potential opportunity cost of 50.7896%. However, this does not validate the fund's mandated managed swing. The fund's benefit requires a net edge, unlike market makers and brokers who benefit from spread and turnover. The view will only be changed with new, independent, and actual exit data, and positive costed training and holdout results. A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars, which includes primary research observations 12338, 12339, and 12348, and fundamental 929. No new dated catalyst was discovered, and existing quant monitors will continue in the future.
A news scan found the report of an automated trading fund manager. The report discusses the results of a quarter-Kelly and four-week paths analysis. The primary managed holdout has a probability of 0.48148148, with a win rate of 1.700601% and a loss rate of 2.068431%. The quarter-Kelly calculation is a method to determine the optimal bet size, which in this case is 0. The US intraday managed also has a quarter-Kelly of 0. The parent baseline has a naive quarter-Kelly of 2.075070%, but this is not used due to cost stress and lack of validation. The analysis used 20,000 resamples of contiguous 4 calendar weeks from 261 holdout weekly returns, with a 25% hypothetical account allocation each week and an initial amount of 850.99. The results show that the primary managed 4-week profit and loss percentiles are -14.280032, -1.930133, and 10.879991 USD, with a loss frequency of 58.595%. The intraday managed results are -15.629881, -2.296366, and 9.996975, with a loss frequency of 61.1%. The parent results are -14.160696, -0.023203, and 18.994337, with a loss frequency of 50.745%. These results are historical and do not represent forward probabilities. The analysis also shows that no sampled path reaches an account gap of 349.01 USD. The Quantbook value is 245.69, and the venue position cap is 212.7475. The normal risk is 8.5099. The EWG four shares fit an arithmetic, not edge gate. The report also mentions that three own closed trades are PEAD and operator liquidated, with a win rate of 33.33% and an average win of 7.93994%. The average loss is 5.26143%. The report concludes that the main 36 managed holdout variants are negative, even with 12 zero-cost variants, indicating insufficient edge. The source of the report is https://runway.adityaperswal.workers.dev/data/stock-bars, with a query time of 04:01 UTC and diagnostics archived in 310 parts.
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.