A news scan found the headline 'PRE91108 frozen February SPY extended execution diagnostic; N1 not an edge test'. The report is about a trading model. The model was fixed and not changed: it used data from 2016 to 2018 to set its parameters. For February 2026, the model gave a weight of 1 and used a lagging average true range of 20, which was 6.2995. On February 2 at 9:30, the model entered a trade at a price of 685.90, plus 15 basis points of slippage, which is 686.92885. The stop loss was 674.32985 and the target was 724.72585. The model also had a quarter target at 712.12685, then broke even the next day. There were fees of 1 basis point and 15 basis points for each side of the trade. The model looked at prices every 15 minutes and used 1178 price points from the start to the end of the trading day on February 5. The model did not have complete data for overnight trading from 8pm to 4am. The trade was exited on February 5 at 10:15 ET at a price of 673.25, which was filled at 672.240125, resulting in a return of -2.157889% and a win rate of 0%. This was not a new test, but rather a review of a previously inspected period. The model did not account for official corrections, bid/ask spreads, or live latency. The model used 10000 units of fractional capital, which matches the vectorbt long-only strategy. The SPY whole share was too expensive for a fund of 187.22. The report came from https://runway.adityaperswal.workers.dev/data/stock-bars and the code is in notes 2265-2268 and research/execution_audit.py.
A news scan found the report titled PRE91108, which is a diagnostic for the execution of a trading strategy on the SPY stock in February. The report states that the prior model was fixed and no parameter sweep was done, with calibration based on variance from 2016 to 2018. The weight for February 2026 was set to 1, with a lag ATR of 20 set to 6.2995. The entry point was on February 2 at 09:30, with an adjusted price of 685.90 and a 15 basis point slippage, resulting in a price of 686.92885. The stop loss was set at 674.32985 and the target at 724.72585. The report also mentions that fees were set at 1 basis point per slip, with 15 basis points each side. The trade was exited on February 5 at 10:15 ET, with a sampled price of 673.25 and a fill price of 672.240125, resulting in a return of -2.157889% and a win rate of 0%. The report notes that this is not a new out-of-sample test, but rather an already-inspected held-out-period execution sensitivity test. The report also states that there is no official correction for bid/ask or live-latency validation. The capital used was a fractional amount of 10000, which reconciles exactly to the vectorbt long-only strategy, as the SPY whole share is unaffordable for a fund of 187.22. The report comes from the source https://runway.adityaperswal.workers.dev/data/stock-bars and references code notes 2265-2268 and research/execution_audit.py.
A news scan found the headline 'PRE91108 frozen February SPY daily execution diagnostic; N1 not an edge test'. This is a diagnostic report for an automated trading fund manager. The report is based on a fixed prior model with no parameter sweep, using calibration data from 2016 to 2018. The model used a weight of 1 and a lag ATR of 20, which was set to 6.2995. On February 2, the model made an entry at 09:30 at a price of 685.90, with a 15 basis point slippage, making the actual entry price 686.92885. The stop loss was set at 674.32985 and the target was set at 724.72585. The model also had a quarter target at 712.12685, and then a breakeven point the next day. The fees for this trade were 1 basis point per trade, plus 15 basis points for slippage on each side. The original daily low triggered a stop on February 2, resulting in a return of -2.000956% and a drawdown of 2.000956%, with a win rate of 0% and N1, which refers to the number of trades. The report notes that this is not a new out-of-sample test, but rather an already-inspected held-out-period execution sensitivity test. There is no official correction for bid/ask or live-latency validation. The report also notes that the explicit fractional capital of 10000 reconciles exactly to the vectorbt long-only backtest, and that buying whole shares of SPY would be unaffordable for a fund of 187.22. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the code notes are available at notes 2265-2268 and research/execution_audit.py.
A post-audit diagnostic test was conducted on the SPY stock with a low price of 68.64 on February 2, 2026. This price was replaced with 678.301862, which is the same-date extended session low of 681.94 adjusted by a daily adjustment factor of 691.70/695.41. This change is not an authoritative correction or a new independent holdout. The original vendor data with 64 cells was retained and quarantined, with 2685 rules unchanged. The monthly rule remains the same, with the first open and prior variance capped at 1, 2 ATR stop, and 6 ATR target, plus 4 ATR quarter and next day break even, with a month-end exit. The test period was from January 3, 2023, to August 31, 2026, with fees of 1 basis point and a slip of 15 basis points per side, and a scale of 2. The return was 39.05693377174397% with a drawdown of 14.068000496405809%, and a win rate of 52.27272727272727%. The mean return was 0.8804737043513139%. The whole policy was executed on a 15-minute basis, but was unvalidated and did not generate any orders. The daily vector backtest match was 3.637978807091713e-12. The training data was unchanged because the outlier was only in 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the research and audit results are in test_vm.py/results.json.
A post-audit review, known as a diagnostic check, was performed on the SPY stock, which is a type of investment fund that tracks the S&P 500 index. On February 2, 2026, the low price of SPY was initially reported as 68.64, but this was updated to 678.301862 in the extended session, which is a period of trading outside regular hours. The daily adjustment factor, a calculation used to adjust prices, was 691.70/695.41, and the 15-minute low was 681.94. This update is not an official correction, but rather a replacement of the original data. The original data from the vendor, which is the company providing the information, was kept, but 2685 cells, or pieces of data, were set aside for further review. The rules for the investment strategy, which include a monthly opening price and a stop-loss order, known as a 1.2 ATR stop, and a target price, known as a 6 ATR target, were not changed. The strategy also includes a quarterly adjustment of 4 ATR, or average true range, and a next-day break-even point. From January 2, 2019, to December 30, 2022, the fees were 1 basis point, or 0.01%, for each transaction, and 15 basis points, or 0.15%, for each slip, which is a difference between the expected and actual price of a trade. The scale, which refers to the size of the investment, was 2. The return on investment was 5.924249409058424%, and the maximum drawdown, or loss, was 22.950594911252875%. The win rate, or percentage of successful trades, was 45.83333333333333%, and the mean, or average, return was 0.620346423396773%. The entire policy, or investment strategy, was based on 15-minute execution, but this was not validated, and no orders were placed. The daily vector, a mathematical representation of the investment, matched the expected result with a very small difference of 3.637978807091713e-12. The training data, which is used to develop the investment strategy, was not changed because the only outlier, or unusual data point, was from 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research and audit results are available in the file research/audit/test_vm.py/results.json.
A news scan found the headline 'Post-audit freeze 2249'. This refers to a review of automated trading decisions. On February 2, 2026, the trading fund manager made a decision about the SPY stock, which is a type of investment that tracks the S&P 500 index, a measure of US stock market performance. The manager replaced a low price of 68.64 with 678.301862, which is 64 times the low price of 681.94 from the same date, adjusted for daily changes. The original information from the vendor, which is the company providing the data, was kept, but some cells were set aside for further review, a process called quarantine. The rules for making trades did not change, and they still consider the first open price of the month and the difference from the previous month, with a stop-loss at 1 or 2 times the average true range, a measure of stock price volatility, and a target at 6 times the average true range. The fund also considers the next day's and quarter's performance. From January 3, 2023, to August 31, 2026, the fund paid fees of 1 basis point, a unit of measurement for interest rates and fees, and 15 basis points for each trade, with a scale of 0.5. The return on investment was 22.137105901782196 percent, with a maximum drawdown, a measure of the largest decline in value, of 6.200989910708743 percent. The fund had 44 trades, with a win rate of 52.27272727272727 percent and an average return of 0.8804737043513142 percent. The entire policy was based on 15-minute execution, but it was not validated, and no orders were placed. The daily vector, a mathematical representation of the trades, matched the expected result with a very small difference of 3.637978807091713e-12. The training data, which is used to make predictions, did not change because the only outlier, an unusual data point, was from 2026. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars and research/audit/test_vm.py/results.json, which appears to be a data file and a research result.
A post-audit review, known as a diagnostic, was performed on a trading model called VM9110600. On February 2, 2026, the model's low price for the SPY stock was updated from 68.64 to 678.301862, which is the same-date extended session low of 681.94 adjusted by a daily adjustment factor of 691.70/695.41. This change is not an official correction and does not affect the model's independent holdout. The original data from the vendor, which includes 64 cells, was retained and quarantined, and rule 2685 was applied. The model's rules remain unchanged, with a monthly first open and prior variance capped at 1, and stop and target levels set at 2 and 6 ATR, respectively, with a quarter adjustment of +4 ATR and a next-day breakeven point. The model also has a month-end exit. From January 2, 2019, to December 30, 2022, the model had fees of 1 basis point and a slip of 15 basis points per side, with a scale of 0.5. The return was -4.314729648196081% with a drawdown of 9.48234794366084%, and 48 trades were made, with a win rate of 45.83333333333333% and a mean of 0.6203464233967744%. The 15-minute execution policy was not validated and no orders were placed. The daily vector backtest match was 3.637978807091713e-12. The training data was not changed because the only outlier was in 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the research and audit results are in research/audit/test_vm.py/results.json.
A post-audit diagnostic test was conducted on February 2, 2026, for the SPY stock, which is a type of stock market index fund. The test replaced the low price of 68.64 with 678.301862, which is the same date extended session low of 681.94 adjusted by a daily adjustment factor of 691.70/695.41. This change is not an authoritative correction or a new independent holdout. The original vendor's 64 cells were retained and quarantined, and the rule remained unchanged. The rule is based on a monthly first open with a prior variance capped at 1, 2 ATR stop, and 6 ATR target, with a 4 ATR quarter and next day break even, and a month end exit. ATR stands for Average True Range, which is a measure of stock price volatility. The test period was from January 3, 2023, to August 31, 2026, with fees of 1 basis point and a slip of 15 basis points per side, and a scale of 1. The return was 34.1951933271146% with a drawdown of 10.5565031388005%, and 44 trades were made with a win rate of 52.27272727272727%. The mean return was 0.8804737043513127%. The whole policy was based on 15-minute execution, but it was not validated and no order was placed. The daily vector backtest match was 1.8189894035458565e-12. The training data was not changed because the outlier was only in 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the research and audit results are in test_vm.py/results.json.
A post-audit review, called a freeze, was done on February 2, 2026, for the SPY stock, which is a type of investment that tracks the S&P 500 index. The low price of SPY on that day was 68.64, but it was replaced with 678.301862, which is the low price from the extended session, a period of trading outside regular hours, adjusted for daily changes. This change is not an official correction and does not affect the independent data used for testing. The original data from the vendor, which provides financial information, was kept, but some of it was set aside for further review. The rules for buying and selling stocks were not changed, including the monthly review of the first opening price and the limits on losses and gains, which are based on a measure of volatility called the Average True Range, or ATR. From January 2, 2019, to December 30, 2022, the fees for buying and selling were 1 basis point, or 0.01%, and 15 basis points for the difference between the expected and actual price, known as the slip. The results showed a return of 5.557496938788775% and a maximum loss, or drawdown, of 14.661439139633735%. The strategy was successful 45.83333333333333% of the time, with an average gain of 0.6203464233967758%. This analysis was based on 15-minute intervals, but it was not validated, meaning it was not checked against actual trades. The daily analysis matched the expected results, with a very small difference of 3.637978807091713e-12. The training data was not changed because the unusual result in 2026 was considered an outlier, or an exception. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the results can be found in research/audit/test_vm.py/results.json, which is a file containing the output of a computer program.
A post-audit diagnostic test, called a freeze, was done on February 2, 2026, for the SPY stock, which had a low price of 68.64. This was replaced with 678.301862, considering the same-day extended session low of 681.94 and a daily adjustment factor of 691.70/695.41. This change is not an official correction or a new independent holdout. The original data from the vendor, with 64 cells, was kept separate, and rule 2685 was applied. The monthly rule remains unchanged, with the first open and prior variance capped at 1 and 2 ATR stop, and 6 ATR target, plus 4 ATR quarter and next day break even, with a month-end exit. From January 3, 2023, to August 31, 2026, fees were 1 basis point and slip was 5 basis points per side, with a scale of 1. The return was 42.575665675294736 percent, with a drawdown of 9.526576307753443 percent, and 44 trades, with a win rate of 56.81818181818182 percent, and a mean of 1.0486536148037133 percent. The whole policy was executed every 15 minutes, but was not validated, and no order was placed. The daily vector backtest match was 3.637978807091713e-12. The training data was not changed because the only outlier was in 2026. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research and audit results can be found in test_vm.py/results.json.
A post-audit review, known as a diagnostic, was performed on a trading model called VM9110600. On February 2, 2026, the model's low price for the SPY stock was updated from 68.64 to 678.30, which is 1862 times the daily adjustment factor of 691.70 divided by 695.41, and also considering the extended session low of 681.94 on the same date. This change is not an official correction and does not affect the model's independent holdout data. The original data from the vendor, which includes 64 cells, has been retained and quarantined, with 2685 rules remaining unchanged. The model's rules include a monthly first open, a stop loss of 1 to 2 times the average true range, or ATR, which is a measure of price volatility, and a target of 6 times the ATR, with a quarter adjustment and a next-day break even, or BE, point, and an exit at the end of the month. From January 2, 2019, to December 30, 2022, the model had fees of 1 basis point, or 0.01%, and a slip of 5 basis points, or 0.05%, per side, with a scale of 1. The return was 9.57%, with a drawdown of 14.34%, and 48 trades, with a win rate of 47.92%, and a mean of 0.75%. The entire policy was executed over 15 minutes, but this has not been validated, and no order was placed. The daily vector backtest match was 3.64e-12. The training data was not changed because the only outlier was in 2026. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars and the research and audit results can be found in the test_vm.py/results.json file.
This is a test of the VM9110600 GLD adapter, which is a tool used to connect and analyze data, specifically a type of financial data called a gold exchange-traded fund, or GLD. The test used a set of rules, called Protocol 2230, with some corrections made in 2233 and 2236. The test looked at data from January 3, 2023, to August 31, 2026, and used data from 2016 to 2018 to calibrate the system, and data from 2019 to 2022 to train it, with a holdout period from 2023 to August 26. The test entered the market monthly, with a limit on how much it could lose, and stopped trading when it lost a certain amount, which is measured in a unit called ATR, or average true range, and also had a target to make a certain amount, which is 6 times the ATR, and an extra 4 ATR for each quarter, with the goal of making a profit by the end of the month. The test also accounted for fees, which were 1 basis point, or 1/100th of a percent, per trade, and a slip of 15 basis points, and assumed that cash had a yield of 0. The results showed a return of 37.26 percent, with a maximum loss of 9.46 percent, and a win rate of 52.27 percent, over 44 months, with 44 trades. The test used a tool called vectorbt for daily reconciliation, which means checking the results every day, and found a maximum error of 3.637978807091713e-12, which is a very small number, with an initial investment of 10,000 units. The test did not validate the results over a 15-minute period or for whole shares, and used a dividend-adjusted proxy, which means it accounted for dividend payments, and did not touch the underlying data. The test was used as a diagnostic instrument, and did not promote a winning strategy. The data came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars, and the results can be found in a file called research/test_vm.py/results.json, which is a type of file that stores data in a format called JSON.
The Protocol 2230 with corrections 2233 and 2236 was used from January 2, 2019, to December 30, 2022, with calibration from 2016 to 2018 and training from 2019 to 2022, holding out data until August 26, 2023. The monthly entry was based on prior variance capped at 1, with a stop at 2 Average True Range (a measure of price volatility) and a target at 6 Average True Range, plus 4 Average True Range per quarter, and the next day's break-even point, with a month-end exit. Fees were 1 basis point (a unit of measurement for interest rates) per side, with a 15 basis point slip, and a cash yield of 0. The return was 8.594624456479094 percent, with a drawdown of 12.863200967894684 percent, a win rate of 47.91666666666667 percent, and 48 trades over 48 months. The vectorbt daily reconciliation had a maximum error of 3.637978807091713e-12 at 10,000 initial units. There was no 15-minute or whole-share validation, and the data used was dividend-adjusted, with a proxy for survivorship bias, and the history was not globally untouched. This was a diagnostic instrument, with no promotion of winning strategies. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results are from research/test_vm.py/results.json.
This is a test of the VM9110600 GLD adapter, which is a tool used to connect and manage data. The test used a set of rules, called Protocol 2230, with some corrections made in 2233 and 2236. The test looked at data from January 3, 2023, to August 31, 2026, and used data from 2016 to 2018 to calibrate the system, and data from 2019 to 2022 for training, with a holdout period from 2023 to August 26. The system made trades at the start of each month, with a limit on how much it could lose, and stopped trading if it lost too much, with a target of making six times the amount of a common measure of market volatility, called ATR. The system also had rules to exit trades at the end of each month. The test included fees of 1 basis point to buy and 5 basis points to sell, and assumed that cash did not earn any interest. The results showed a return of 43.29%, with a maximum loss of 8.48%, and 52.27% of trades were winners, out of 44 trades over 44 months. The test also checked the accuracy of the system, with a maximum error of 3.637978807091713e-12, using an initial investment of 10,000 units. The test did not validate the results against 15-minute data or whole-share data, and used a proxy that was adjusted for dividends, with a note that the data may not be perfect. The test was run as a diagnostic tool, without promoting any winning strategies. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars, and the results can be found in research/test_vm.py/results.json.
This is a description of an automated trading system. The system uses a protocol called Protocol2230 with corrections 2233 and 2236. It was calibrated using data from 2016 to 2018 and trained on data from 2019 to 2022, with a holdout period from 2023 to August 26. The system enters a trade monthly, with a position size capped at 1 times the prior variance. It stops trading at 2 times the average true range, with a target of 6 times the average true range, and adds 4 times the average true range to the next day's closing price. The system exits at the end of the month. The fees are 1 basis point per trade, with a 5 basis point slip per side, and the cash yield is 0. The return is 13.220439532381523 percent, with a maximum drawdown of 13.176694279509316 percent, and a win rate of 47.91666666666667 percent, over 48 months. The system uses a daily reconciliation with a maximum error of 3.637978807091713e-12, at an initial investment of 10000 units. The system does not validate 15-minute or whole-share data, and uses a dividend-adjusted proxy, with survivorship bias, and a history that has not been globally untouched. This is a diagnostic instrument, and there is no promotion of winning trades. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research and test results are from research/test_vm.py/results.json.
This is a report on a test of the VM9110600 GLD strategy, which uses a protocol called Protocol2230 with corrections from 2233 and 2236. The test ran from January 3, 2023, to August 31, 2026, and was calibrated using data from 2016 to 2018 and trained on data from 2019 to 2022, with a holdout period from 2023 to August 26. The strategy rebalances on the first monthly open and has a constant calibration exposure, with terminal liquidation. The test looked at the win frequency of the strategy separately each month, and applied fees of 1 basis point per side, with a 15 basis point slip, and assumed a cash yield of 0. The return on investment was 117.6517761730966 percent, with a maximum drawdown of 23.90681734597957 percent, and a win rate of 100 percent over 44 months. The test used a daily reconciliation with a maximum error of 0, and started with 10,000 initial units. The test did not validate the results over 15-minute intervals or whole shares, and used a dividend-adjusted proxy, with survivorship bias, and a history that was not globally untouched. This test is a diagnostic instrument and does not promote any winning strategy. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results can be found in research/test_vm.py/results.json. A news scan found the headline was not available, this is a raw script output.
This is a description of an automated trading fund manager's notes. The protocol used is Protocol 2230 with corrections 2233 and 2236. The time period for this data is from January 2, 2019, to December 30, 2022, with calibration done from 2016 to 2018 and training from 2019 to 2022, and holdout from 2023 to August 26. The fund is rebalanced on the first monthly open with constant calibration exposure and terminal liquidation. The position is labeled as N1, and the monthly win frequency is calculated separately. The fees are 1 basis point per side, with a 15 basis point slip, and the cash yield is 0. The return is 34.92247805890547 percent, with a drawdown of 19.745224675675132 percent, and a win rate of 100 percent over 48 months. The vectorbt daily reconciliation has a maximum error of 0 at 10,000 initial units. There is no 15-minute or whole-share validation, and the data is dividend-adjusted and subject to survivorship bias, but the history has not been globally untouched. This is a diagnostic instrument, and there is no winner promotion. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research and test results are from research/test_vm.py/results.json.
This is a report on a test of the VM9110600 GLD strategy, which uses a protocol called Protocol2230 with corrections 2233 and 2236. The test ran from January 3, 2023, to August 31, 2026, using data from 2016 to 2018 for calibration and from 2019 to 2022 for training, with a holdout period from 2023 to August 26. The strategy rebalances on the first monthly open with constant calibration exposure and terminal liquidation. The test tracked the position, monthly win frequency, and other metrics separately. It applied fees of 1 basis point per side and a 5 basis point slip, with a cash yield of 0. The results showed a return of 118.07085269270567% and a drawdown of 23.903175817429066%, with a win rate of 100% over 44 months. The test used a daily reconciliation with a maximum error of 0 at 10,000 initial units. The data did not include 15-minute or whole-share validation, but used a dividend-adjusted proxy and accounted for survivorship bias, without globally untouched history. This is a diagnostic instrument and does not promote a winning strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the research results are in research/test_vm.py/results.json. A news scan found the source to be a JSON output.
This is a description of an automated trading fund manager's notes. The protocol used is Protocol 2230 with corrections 2233 and 2236, which was applied from January 2, 2019, to December 30, 2022, after being calibrated from 2016 to 2018 and tested from 2019 to 2022, with a holdout period from 2023 to August 26. The fund was rebalanced on the first monthly open with constant calibration exposure and terminal liquidation. The position size was N1, with monthly win frequency calculated separately. The fees were 1 basis point per side, with a 5 basis point slip, and cash had a 0 yield. The return was 35.18074489362362 percent, with a drawdown of 19.73934171175278 percent, and a win rate of 100 percent over 48 months. The fund used a daily reconciliation with a maximum error of 0 at 10,000 initial units. There was no 15-minute or whole-share validation, and the data used was dividend-adjusted and subject to survivorship bias, but the history was not globally untouched. This was a diagnostic instrument, and there was no winner promotion. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was based on the file research/test_vm.py/results.json.
A news scan found the headline is not available, but a data line was found. The data line describes a test of a trading strategy called VM9110600 on the GLD stock from 2023-01-03 to 2026-08-31, using data from 2016 to 2018 for calibration and 2019 to 2022 for training, with a holdout period from 2023 to August 26. The strategy uses a protocol called Protocol2230 with corrections 2233 and 2236. The test was run with a monthly rebalance, where the first monthly open is used to rebalance the portfolio, with a capped prior variance of 1. The strategy also includes terminal liquidation, which means selling all assets at the end of the test. The test tracked the win frequency, which is the number of times the strategy made a profit, separately for each position, referred to as PositionN1. The fees for the strategy were 1 basis point, or 0.01%, per side, with an additional 15 basis points, or 0.15%, for the slip, which is the difference between the expected and actual price of a trade, and a cash yield of 0, meaning the strategy did not earn any interest on its cash holdings. The return on investment was 82.09329801816475%, with a maximum drawdown, which is the largest loss from peak to trough, of 10.650372817766629%, and a win rate of 100%. The strategy was tested over 44 months, with a daily reconciliation, which is a process of checking the strategy's performance, and a maximum error of 0, using 10,000 initial units. The test did not include validation of 15-minute intervals or whole-share validation, and used a dividend-adjusted proxy, which takes into account the dividends paid by the stock, and accounted for survivorship bias, which is the tendency of successful stocks to remain in the test, and did not use a globally untouched history, which means the test used real historical data. The test was run as a diagnostic instrument, and did not promote a winning strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was conducted using the file research/test_vm.py/results.json.
This is a report on a trading strategy called VM9110600 GLD. The strategy was tested using data from 2016 to 2022 and then applied from 2019 to 2022, with the remaining period held out for testing. The strategy was rebalanced on the first day of each month, with a cap on prior variance. The strategy had a return of 22.780768644632985% and a drawdown of 17.556571194272973%. The strategy won 100% of the time over 48 months. The fees were 1 basis point per side, with an additional 15 basis points for slippage. The cash yield was 0. The strategy was tested using a daily reconciliation with a maximum error of 7.275957614183426e-12. The test used 10,000 initial units. The data used was dividend-adjusted and included survivorship bias, but was not globally untouched. The strategy was not validated for 15-minute intervals or whole-share trading. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the results can be found in research/test_vm.py/results.json.
This is a report on a trading test called VM9110600 GLD. The test used a set of rules called Protocol2230 with some corrections, which were made on specific dates. The test was run from January 3, 2023, to August 31, 2026, using data from 2016 to 2018 to calibrate the system and data from 2019 to 2022 for training, with the remaining data held out for testing until August 26, 2023. The test rebalanced its portfolio on the first day of each month, using a method that considers past volatility, with a cap on how much it can change. The test also had a rule to liquidate everything at the end of the test. It tracked the frequency of winning months separately for each position. The test charged a fee of 1 basis point for management and 5 basis points for each trade, with cash having a 0% yield. The results showed a return of 84.12249185590099% with a maximum drawdown of 10.591120908468277%, and 100% of the months were winning months out of 44 months. The test used a daily check to ensure accuracy, starting with 10,000 units, and did not validate its results against 15-minute data or whole-share data. It used a method to adjust for dividends and ensured that the data used was consistent over time, without globally untouched history. This test was used as a diagnostic tool and did not promote any winning strategy. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the results can be found in research/test_vm.py/results.json.
This is a report on the performance of the VM9110600 GLD monthly trading strategy. The strategy was tested using historical data from 2016 to 2022, with the years 2016-2018 used for calibration and 2019-2022 used for training, and the period from 2023 to August 26 used as a holdout period. The strategy uses a protocol called Protocol2230 with corrections 2233 and 2236. The strategy rebalances on the first monthly open, with a capped prior variance of 1, and terminal liquidation. The position size is denoted as N1, and the monthly win frequency is calculated separately. The fees for the strategy are 1 basis point per side, with a 5 basis point slip, and cash has a 0 yield. The return for the strategy is 24.385365922605274 percent, with a drawdown of 17.386666203685543 percent, and a win rate of 100 percent over 48 months. The strategy was tested using vectorbt daily reconciliation, with a maximum error of 7.275957614183426e-12 at 10000 initial units. The data used for the strategy does not include 15-minute or whole-share validation, and is dividend-adjusted and subject to survivorship bias, with the history not globally untouched. This is a diagnostic instrument, and there is no winner promotion. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research and test results can be found in research/test_vm.py/results.json.
This is a test of the TLT adapter, specifically the slip15 scale1 configuration. The test used a protocol with corrections from 2230, 2233, and 2236, and was run from January 3, 2023, to August 31, 2026, with calibration from 2016 to 2018 and training from 2019 to 2022, and a holdout period from 2023 to August 26. The test entered the market monthly, with a capped prior variance of 1, and used a stop loss of 2 ATR and a target of 6 ATR, with an additional 4 ATR added quarterly and a next-day break-even point, and exited at the end of each month. The fees were 1 basis point per side, with a 15 basis point slip, and a cash yield of 0. The return was -14.88%, with a drawdown of 18.09%, and a win rate of 38.64%, over 44 months, with 44 trades. The test was run using vectorbt, with daily reconciliation and a maximum error of 1.8189894035458565e-12, at an initial unit size of 10,000. The test did not include 15-minute or whole-share validation, and used a dividend-adjusted proxy, with survivorship bias, and a history that was not globally untouched. This is a diagnostic instrument, and there is no winner promotion. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was conducted using the test_vm.py script, with results stored in results.json.
The trading protocol used corrections 2233 and 2236. The data used was from January 2, 2019, to December 30, 2022, with calibration from 2016 to 2018 and a holdout period from 2023 to August 26. The monthly entry was based on prior variance capped at 1, with a stop at 2 Average True Range (a measure of price volatility) and a target at 6 Average True Range, plus 4 Average True Range for the next quarter, and a buy exit at the end of the month. Fees were 1 basis point (a unit of measurement for interest rates) per side, with a 15 basis point slip, and a cash yield of 0. The return was -16.31%, with a drawdown of 19.12%, a win rate of 27.08%, and 48 trades over 48 months. The vectorbt daily reconciliation had a maximum error of 3.637978807091713e-12 at 10,000 initial units. There was no 15-minute or whole-share validation, and the data used was dividend-adjusted and subject to survivorship bias (the tendency for successful companies to be overrepresented in historical data), but the history was not globally untouched. This was a diagnostic instrument, with no winner promotion. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was based on the test_vm.py/results.json file.