A stock diagnostic for SLV was conducted at 06UTC with a fixed downside-regime approach. The premises of this diagnostic are: the signal at close must fill the next open, holdout results cannot be selected for an option payoff, and stops are not part of this diagnostic. A frozen note, number 963, had conditions including a negative 5-day return, being below the 50-day Simple Moving Average (SMA), and having a 20-day Realized Volatility (RV) greater than the 60-day RV. The diagnostic entered the next open and exited 10 sessions later at the open, with neighboring 5 and 20 session sensitivity. The diagnostic used non-overlapping trades, with no incomplete terminal trades, and was trained on data from 2022 to 2025, with 2026 as the holdout year. The diagnostic used vectorbt, with explicit open price, fees of 5 basis points, and slippage of 10 basis points on each side, with 1000 normalized all-in equity. Daily close marking missed intraday drawdown. The trade count and profit included net costs, and no new stock proposal was authorized by this test. The primary train results were: symbol SLV, period train, hold 10, return 0.3927247826723033, drawdown 23.355444704194184, number of trades 18, profit 0.3888888888888889, win 5.436416972857905, and loss 3.224960097832085. The holdout results were: symbol SLV, period test, hold 10, return 2.548621934408675, drawdown 16.825986420917992, number of trades 4, profit 0.5, win 8.493313943206598, and loss 6.3882640387378355. All variants of the diagnostic were: a list of results with different hold periods, including 5, 10, and 20 sessions. The results showed that the ETHA and IBIT had shorter inception histories. The training losses and holdout uncertainty prevented independent robust edge, and positive isolated variants were not adopted. The actual option holdout was 0, and the bearish stock-regime/rebound test did not price a put or validate a call. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was analyzed using the research/analyze.py script. A separate mean-reversion test on XLF showed a gross return of 3.244%, a drawdown of 1.622%, 7 trades, and a win rate of 85.714% over 251 bars.
This is a report on a stock diagnostic test for GLD, a type of investment fund, as of 06UTC. The test has three main premises: 1) a signal to buy or sell must be filled at the next open, 2) the results of the test cannot be used to select an option payoff, and 3) stops, which are orders to buy or sell when a certain price is reached, are not part of this test. The test uses a set of rules, including a negative 5-day return and a price below the 50-day simple moving average, to decide when to enter and exit the market. The test was run on data from 2022 to 2025, and the results are being held out for 2026. The test uses a tool called vectorbt to analyze the data, taking into account fees of 5 basis points and slippage of 10 basis points on each side, and assuming an initial investment of 1000 units of equity. The results of the test include a return of 5.5362% and a drawdown of 8.5099% during the training period, and a return of -6.9721% and a drawdown of 9.0576% during the holdout period. The test also includes a list of variants, which are different versions of the test with different parameters, such as holding periods of 5, 10, and 20 sessions. The results of these variants are also included. The test is not meant to be a robust edge, or a reliable way to make investment decisions, due to the short history of the data and the uncertainty of the results. The test is also not meant to validate a call or price a put, which are types of investment options. The data for the test comes from a source called runway.adityaperswal.workers.dev, and the test was run using a tool called research/analyze.py. Additionally, a separate test was run on XLF, which is a type of investment fund, using a mean-reversion strategy, which resulted in a gross return of 3.244% and a drawdown of 1.622%.
This is a report from an automated trading fund manager. The report is about a stock diagnostic test for TLT, which is a type of investment, specifically a bond fund. The test has three main premises: 1) a signal to buy or sell must be filled at the next open, 2) the results of the test cannot be used to select an option payoff, and 3) stops, which are orders to buy or sell when a stock reaches a certain price, are not part of this test. The test uses a set of rules, including a negative 5-day return and a price below the 50-day simple moving average, or SMA, which is a measure of a stock's average price over a certain period. The test also uses a measure called RV20 and RV60, which are types of volatility, or how much a stock's price changes. If these conditions are met, the test enters a trade at the next open and exits 10 sessions later. The test uses a technique called vectorbt, which is a way of analyzing and backtesting trading strategies. The test assumes a fee of 5 basis points, or 0.05%, and a slippage of 10 basis points, or 0.1%, on each trade. The test uses a total of 1000 units of equity, which is the total value of the investments. The test is divided into two periods: a training period from 2022 to 2025, and a holdout period in 2026. The results of the test are as follows: during the training period, the test had a return of -14.62% and a maximum drawdown, or loss, of 23.24%. The test had 28 trades, with a win rate of 42.86%. During the holdout period, the test had a return of -3.04% and a maximum drawdown of 6.58%. The test had 6 trades, with a win rate of 33.33%. The test also analyzed different variants, with hold periods of 5, 10, and 20 sessions. The results of these variants are listed. The test also mentions that the results are not robust, meaning they may not be reliable, due to the short history of the data and the small number of trades. The test is not adopting any new strategies based on these results. The source of the data is a website, and the test uses a Python script to analyze the data. The test also mentions a separate analysis of the stock XLF, which had a mean reversion, or a return to its average price, of 3.244%.
This is a report on a stock diagnostic test for the EEM stock. The test has three main premises: 1) a signal at the close of a trading day must be filled at the next open, 2) the results of the test cannot be used to select an option payoff, and 3) stops are not part of this test. The test uses a specific set of rules, including a negative 5-day return and a price below the 50-day Simple Moving Average (SMA), which is a calculation of the average price of a stock over a certain period of time. If these conditions are met, the test enters a trade at the next open and exits 10 sessions later at the open. The test also considers the sensitivity of the trade over the neighboring 5 and 20 sessions. The test was trained on data from 2022 to 2025 and tested on data from 2026. The results of the test are as follows: for the training period, the return was -3.438617505551065%, the maximum drawdown was 15.595172168610748%, and the win rate was 0.5294117647058824. For the test period, the return was -3.7926818201905688%, the maximum drawdown was 11.404525868136561%, and the win rate was 0.5. The test also considered different variants, including holding periods of 5, 10, and 20 sessions. The results of these variants are listed below. The test used a vectorbt system, which is a backtesting framework, with explicit open prices, fees of 5 basis points, and slippage of 10 basis points on each side. The test also used a normalized all-in equity of 1000. A news scan found the headline 'ETHA/IBIT shorter inception histories', which refers to the shorter history of the ETHA and IBIT stocks. The test did not adopt any of the variants due to training losses or holdout uncertainty. The test also did not price a put or validate a call. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EEM&timeframe=1Day&feed=sip&adjustment=all and the research/analyze.py script. A separate test on the XLF stock using a mean-reversion strategy resulted in a gross return of 3.244%, a maximum drawdown of 1.622%, and a win rate of 85.714% over 251 bars.
A news scan found the headline '06UTC XLE fixed downside-regime stock diagnostic, 2026 holdout'. The premises of this diagnostic are: 1) a signal at close must fill the next open, 2) holdout results cannot be selected for an option payoff, and 3) stops are not part of this fixed-horizon stock diagnostic. The diagnostic uses a rule called 'frozen note963', which means a negative 5-day return and a price below the 50-day Simple Moving Average (SMA), and a 20-day Realized Volatility (RV20) greater than the 60-day Realized Volatility (RV60). When this rule is met, the diagnostic enters a trade at the next open and exits 10 sessions later at the open. The diagnostic also considers the sensitivity of the neighboring 5 and 20 sessions. The trades are non-overlapping, and there are no incomplete terminal trades. The diagnostic was trained on data from 2022 to 2025 and tested on data from 2026. The diagnostic uses a tool called'vectorbt' to explicitly calculate the open price, fees of 5 basis points, and slippage of 10 basis points on each side, with 1000 normalized all-in equity. The diagnostic marks the daily close and misses intraday drawdown. The trade count and profit/loss include net costs. The primary train results for XLE are: return of 90.97%, drawdown of 15.53%, 26 trades, and a profit/loss ratio of 0.73. The holdout results for XLE are: return of 10.70%, drawdown of 4.63%, 4 trades, and a profit/loss ratio of 0.75. The diagnostic also tested variants with different hold periods: 5, 10, and 20 sessions. The results for these variants are: for a 5-session hold, return of 28.74% and drawdown of 17.09% in the train period, and return of 3.45% and drawdown of 2.86% in the test period; for a 10-session hold, return of 90.97% and drawdown of 15.53% in the train period, and return of 10.70% and drawdown of 4.63% in the test period; for a 20-session hold, return of 64.67% and drawdown of 16.74% in the train period, and return of -3.42% and drawdown of 9.84% in the test period. The diagnostic notes that ETHA and IBIT have shorter inception histories. The training loses or holdout uncertainty/small N prevents independent robust edge, and positive isolated variants are not adopted. The actual option holdout is 0, and a bearish stock-regime/rebound test neither prices a put nor validates a call. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=XLE&timeframe=1Day&feed=sip&adjustment=all, and the research/analyze.py script was used. The CLI XLF mean-reversion separately shows a gross return of 3.244%, drawdown of 1.622%, 7 trades, and a win rate of 85.714%, with 251 bars, audited with same close fill and no explicit costs, and exit ignored, not validation.
A news scan found the headline '06UTC XLF fixed downside-regime stock diagnostic, 2026 holdout'. This is a report about a stock diagnostic test on XLF. The test had three premises: 1) a signal at close must fill the next open, 2) holdout results cannot be selected for an option payoff, and 3) stops are not part of this fixed-horizon stock diagnostic. The test used a specific set of rules, including entering a trade at the next open if the 5-day return was negative and the stock was below its 50-day simple moving average, and exiting 10 sessions later at the open. The test also considered the sensitivity of the previous 5 and 20 sessions. The test was run on data from 2022 to 2025 for training and 2026 for holdout. The results showed a return of 6.639887450161826% and a drawdown of 13.221007687259211% during the training period, with 23 trades and a win rate of 0.5652173913043478. In the holdout period, the return was -6.455030075496722% and the drawdown was 7.615842236544101%, with 4 trades and a win rate of 0%. The test also considered variants with different hold periods, including 5, 10, and 20 sessions. The results of these variants are listed. The test used a tool called vectorbt and considered fees of 5 basis points and slippage of 10 basis points on each side. The test also used a normalized all-in equity of 1000. The results are based on daily close prices and do not account for intraday drawdown. The test did not propose any new stocks and did not validate any options. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=XLF&timeframe=1Day&feed=sip&adjustment=all and the test was run using a script called research/analyze.py. A separate test using a command-line interface found a mean reversion of 3.244% and a drawdown of 1.622% with 7 trades and a win rate of 85.714% over 251 bars.
A command was run on a computer program to test a trading strategy called breakout on seven cryptocurrency pairs, including BTC/USD, ETH/USD, and others, from a certain point in time to September 8, using a 20-day lookback period, 615 days of data, a 3% stop loss, and a 4.5% target profit. The program used closing prices to generate signals and did not account for execution costs. The results showed gross returns of 2.3668% for BTC, -20.7311% for ETH, 27.8476% for SOL, 6.0689% for XRP, -34.6682% for UNI, -29.1255% for LTC, and 8.1187% for LINK, with the number of trades closed ranging from 23 to 40. A full breakdown of the results is available. However, the results are not reliable due to limitations in the testing method, and a more comprehensive approach is needed to determine the effectiveness of the strategy. The data used for the test came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. This test does not provide evidence that the compression method works.
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.