This is a test of the VM9110600 EEM adapter, which is a type of automated trading system. The test used a protocol called Protocol2230 with corrections 2233 and 2236, and it ran from January 3, 2023, to August 31, 2026. The system 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 made monthly entries based on prior variance, with a cap of 1, and used a stop loss of 2 ATR, or average true range, and a target of 6 ATR, with an additional 4 ATR added each quarter and on the next day's break even point, and exited at the end of each month. The fees were 1 basis point per side, with a slip of 15 basis points, and the cash yield was 0. The return was 12.88 percent, with a drawdown of 18.85 percent, and a win rate of 47.73 percent, over 44 months and 44 trades. The system used a daily reconciliation with a maximum error of 3.637978807091713e-12, and an initial investment of 10,000 units. 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 results are in the file research/test_vm.py/results.json.
This is a description of a trading strategy, which is a set of rules for buying and selling stocks. The strategy uses a protocol called Protocol2230 with corrections 2233 and 2236, which were applied from January 2, 2019, to December 30, 2022. The strategy was calibrated, or tested, using data from 2016 to 2018, and then trained, or learned, using data from 2019 to 2022, with a holdout period from 2023 to August 26. The strategy enters a trade, or buys a stock, on a monthly basis, with a limit on how much it can lose, which is capped at 1 times the prior variance, a measure of how much the stock price has changed in the past. The strategy also has a stop loss, which is set at 2 times the average true range, a measure of how much the stock price has changed, and a target, or profit goal, of 6 times the average true range, with an additional 4 times the average true range added each quarter, and the trade is exited at the end of the month or the next day if a certain condition is met, called a breakout even, or BE. The strategy has fees of 1 basis point, a measure of the cost of trading, and 15 basis points per trade, and it does not earn any interest on cash, which is a yield of 0. The strategy had a return of -24.76 percent and a maximum drawdown, or loss, of 44 percent, with a win rate of 33.33 percent, over 48 months, with 48 trades. The strategy was tested using a tool called vectorbt, which checks the strategy's performance daily, and it found a maximum error of 3.637978807091713e-12, which is a very small number, using an initial investment of 10,000 units. The strategy does not validate its trades using 15-minute intervals or whole shares, and it uses a dividend-adjusted proxy, which means it takes into account the dividends paid by the stocks, and it does not account for survivorship bias, which means it does not exclude stocks that have gone out of business, and it uses a history that has not been globally untouched, meaning it has been adjusted in some way. The strategy is a diagnostic instrument, which means it is used to test and evaluate, and it does not promote winning trades. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research and test results are in a file called research/test_vm.py/results.json.
This is a test of the VM9110600 EEM adapter, specifically the slip5 scale1 configuration. The test used a protocol with corrections from 2230, 2233, and 2236, and the data range was from 2023-01-03 to 2026-08-31, with calibration from 2016 to 2018 and training from 2019 to 2022, and a holdout period from 2023 to August 26. The test had a monthly entry with a capped prior variance of 1, and a stop loss of 2 ATR, or Average True Range, which is a measure of volatility, with a target of 6 ATR, and an additional 4 ATR for the next quarter, with a next-day break-even point and a month-end exit. The fees were 1 basis point, or 0.01%, per side, with an additional 5 basis points for slippage, and a cash yield of 0. The return was 19.83%, with a maximum drawdown of 17.78%, and a win rate of 47.73%, over 44 months, with 44 trades. The test used a daily reconciliation with a maximum error of 3.637978807091713e-12, at an initial investment of 10000 units. 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 data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results are from research/test_vm.py/results.json.
This is a report on the performance of the VM9110600 EEM adapter. The protocol used for corrections is Protocol2230 with corrections 2233 and 2236. The data used for calibration was from 2016 to 2018, and the training data was from 2019 to 2022, with a holdout period from 2023 to August 26. The monthly entry was based on prior variance capped at 1, with a stop loss at 2 ATR and a target at 6 ATR, and an additional 4 ATR for the next quarter, with a buy and exit at the end of the month. The fees were 1 basis point per side, with a 5 basis point slip, and a cash yield of 0. The return was -14.87%, with a drawdown of 42.92%, and a win rate of 35.42%, over 48 months, with 48 trades. The vectorbt daily reconciliation had a maximum error of 3.637978807091713e-12, at 10000 initial units. There was no 15-minute or whole-share validation, and the data used was dividend-adjusted, with survivorship bias, and the history was not globally untouched. This is a diagnostic instrument, with no winner promotion. 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.
A news scan found the headline about a test of VM9110600 EEM with constant parameters. The test used Protocol 2230 with corrections 2233 and 2236, and it ran from January 3, 2023, to August 31, 2026. The test was calibrated using data from 2016 to 2018, trained on data from 2019 to 2022, and held out data from 2023 to August 26. The test rebalanced on the first monthly open with constant calibration exposure and terminal liquidation. It tracked the position of N1 and the monthly win frequency separately. The fees were 1 basis point per side, with a 15 basis point slip, and the cash yield was 0. The return was 73.49998728871863 percent, with a drawdown of 14.972165090313162 percent, a win rate of 100 percent, and 44 months of data. The test used vectorbt for daily reconciliation with a maximum error of 0 at 10,000 initial units. The test did not validate 15-minute or whole-share data and used a dividend-adjusted proxy with survivorship bias, and the history was not globally untouched. The test was a diagnostic instrument and did not promote a winner. 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.
This is a description of a trading protocol, specifically Protocol 2230 with corrections 2233 and 2236. The trading period is from January 2, 2019, to December 30, 2022, with calibration done from 2016 to 2018 and a holdout period from 2023 to August 26. The strategy involves rebalancing on the first monthly open with constant calibration exposure and terminal liquidation. The position size is denoted as N1, with monthly win frequency calculated separately. The fees are 1 basis point per side, with a 15 basis point slip, and cash has a 0 yield. The return is 6.9403397381326615 percent, with a drawdown of 35.27627543391352 percent, a win rate of 100 percent, and 48 months of trading. The strategy uses vectorbt for daily reconciliation with a maximum error of 0 at 10,000 initial units. There is no validation for 15-minute or whole-share trades, and the data is dividend-adjusted with a proxy for survivorship, but the history has not been globally untouched. This is a diagnostic instrument, and there is no promotion of winning strategies. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results can be found in research/test_vm.py/results.json.
This is a report on a test of the VM9110600 strategy, which is a set of rules for buying and selling stocks. The test was run from 2023-01-03 to 2026-08-31, using data from 2016 to 2018 to calibrate the strategy and data from 2019 to 2022 to train it, with the remaining data held out for testing until August 26, 2023. The strategy was rebalanced on the first day of each month, with the goal of maintaining constant exposure, and all positions were liquidated at the end of the test. The test looked at the performance of the strategy separately for each position and for the frequency of monthly wins. The strategy was charged a fee of 1 basis point, or 0.01%, per trade, with an additional 5 basis point slip, and cash had a 0% yield. The strategy returned 73.82%, with a maximum drawdown of 14.97%, and won 100% of the time, with 44 months of data. The test used a daily reconciliation with a maximum error of 0, starting with 10,000 units, and did not validate the results against 15-minute data or whole-share data. The test also used a dividend-adjusted proxy and accounted for survivorship bias, but did not use globally untouched data. This test was run as a diagnostic instrument and did not promote a winning strategy. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars and the results can be found in research/test_vm.py/results.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, and holdout from 2023 to August 26. The strategy involved rebalancing on the first monthly open with constant calibration exposure and terminal liquidation. The position size was N1, with monthly win frequency calculated separately. Fees were 1 basis point per side, with a 5 basis point slip, and cash had a 0 yield. The return was 7.143491049706818 percent, with a drawdown of 35.27153425719338 percent, a win rate of 100 percent, and 48 months of data. The vectorbt daily reconciliation had a maximum error of 0 at 10,000 initial units. There was no 15-minute or whole-share validation, and the data was dividend-adjusted, with survivorship bias, and the history was not globally untouched. This was a diagnostic instrument, with no winner promotion. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results can be found in research/test_vm.py/results.json.
This is a report on a test of the VM9110600 strategy, which is a set of rules for an automated trading fund manager. The test used data from 2016 to 2022 to train the strategy and data from 2023 to August 26, 2023, to test it. The strategy was rebalanced on the first monthly open, with a cap on prior variance of 1. The test also included terminal liquidation, which means selling all positions at the end of the test. The results showed a return of 68.54% and a maximum drawdown of 15.55%. The strategy had a win frequency of 100% over 44 months. The test used a fee of 1 basis point per side, with a slip of 15 basis points. The initial investment was 10,000 units. The test also included daily reconciliation with a maximum error of 1.8189894035458565e-12. The data used was dividend-adjusted and included survivorship bias, but was not globally untouched. The test was run using the vectorbt library and the results can be found at https://runway.adityaperswal.workers.dev/data/stock-bars and research/test_vm.py/results.json. A news scan found the source of the data.
This is a report on the performance of the VM9110600 EEM monthly train slip15 scale1. The protocol used for corrections is Protocol2230 with corrections made on dates 2233 and 2236. The data used for calibration was from 2016 to 2018, and the training data was from 2019 to 2022, with a holdout period from 2023 to August 26. The first monthly open rebalance was done with a capped prior variance of 1 and terminal liquidation. The position N1 and monthly win frequency were calculated separately. The fees were 1 basis point per side, with a slip of 15 basis points, and a cash yield of 0. The return was -0.9303762412577687 percent, with a drawdown of 31.475732190328763 percent, and a win rate of 0 percent over 48 months. The vectorbt daily reconciliation had 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, with survivorship bias, and the history 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 and test results are from research/test_vm.py/results.json.
This is a report on a trading test called VM9110600 EEM. The test used a set of rules called Protocol 2230 with corrections 2233 and 2236. The test ran 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 the portfolio on the first day of each month, using a method to limit risk called capping prior variance at 1, and the portfolio was liquidated at the end of the test. The test looked at the performance of the first position, called Position N1, and the frequency of monthly wins separately. The test included fees of 1 basis point for each transaction and a 5 basis point slip per side, with cash having a 0% yield. The return on investment was 69.42229454318145% with a maximum drawdown of 15.518137983819958%, and 100% of the months were winning months, with 44 months of data. The test used a daily reconciliation method with a maximum error of 7.275957614183426e-12, starting with 10,000 initial units. The test did not validate 15-minute intervals or whole-share trading, and used a dividend-adjusted proxy with survivorship bias, without altering the global history. 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 test results can be found in research/test_vm.py/results.json.
This is a report on the performance of the VM9110600 EEM monthly train slip5 scale1. The protocol used for corrections is Protocol2230 with corrections 2233 and 2236, and the data range is from 2019-01-02 to 2022-12-30, with calibration done from 2016 to 2018 and training from 2019 to 2022, and holdout from 2023 to August 26. The first monthly open rebalance is done with prior variance capped at 1 and terminal liquidation. Position N1 and monthly win frequency are calculated separately. Fees are 1 basis point per side, with a slip of 5 basis points, and cash has a 0 yield. The return is -0.048481014134449385 percent, with a drawdown of 31.193884881492362 percent, and a win rate of 0 percent, with 48 months of data. The vectorbt daily reconciliation has a maximum error of 1.8189894035458565e-12 at 10000 initial units. There is no 15-minute or whole-share validation, and the data is dividend-adjusted with a proxy for survivorship, and the history has not been 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 are from research/test_vm.py/results.json.
This is a test of the VM9110600 EFA adapter, specifically the slip15 scale1 configuration. The test used the Protocol 2230 with corrections 2233 and 2236, and ran from 2023-01-03 to 2026-08-31, 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 test made monthly entries based on prior variance, capped at 1, with a stop loss at 2 ATR and a target at 6 ATR, and also considered a quarter with a next-day break even point and a month-end exit. The fees were 1 basis point per side, with a 15 basis point slip and a cash yield of 0. The test resulted in a return of 4.277085141387316 percent, with a drawdown of 17.797992253073534 percent, a win rate of 45.45454545454545 percent, and 44 trades over 44 months. The test used vectorbt for daily reconciliation, with a maximum error of 3.637978807091713e-12 at 10,000 initial units. The test did not include 15-minute or whole-share validation, and used a dividend-adjusted proxy, with survivorship bias and a non-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 from research/test_vm.py/results.json.
A news scan found the headline is not available, but a data line was found with the following information: The Protocol 2230 with corrections 2233 and 2236 was used from 2019-01-02 to 2022-12-30, with calibration from 2016 to 2018 and training from 2019 to 2022, and holdout from 2023 to August 26. The monthly entry was based on prior variance capped at 1, with a stop at 2 ATR, or Average True Range, which is a measure of volatility, and a target at 6 ATR, with an additional 4 ATR for the next quarter and a next-day break-even point, and an exit at the end of the month. The fees were 1 basis point, or 0.01%, per side, with a 15 basis point slip, and a cash yield of 0. The return was -19.25% with a drawdown of 26.85%, a win rate of 39.58%, and 48 trades over 48 months. The vectorbt daily reconciliation had a maximum error of 1.8189894035458565e-12 at 10,000 initial units. There was no 15-minute or whole-share validation, and the data was dividend-adjusted and subject to survivorship bias, but the history was not 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/test_vm.py/results.json file.
This is a test of the VM9110600 EFA adapter, specifically the slip5 scale1 configuration. The test used a protocol called Protocol2230 with corrections 2233 and 2236, and the data range was from 2023-01-03 to 2026-08-31, with calibration from 2016 to 2018 and training from 2019 to 2022, and a holdout period from 2023 to August 26. The test had a monthly entry based on prior variance capped at 1, and a stop loss at 2 ATR, or average true range, with a target at 6 ATR, and an additional 4 ATR for the next quarter, with a buy and exit at the end of the month. The fees were 1 basis point per side, with a 5 basis point slip, and a cash yield of 0. The return was 11.00021993637197 percent, with a drawdown of 16.478975701870823 percent, and a win rate of 45.45454545454545 percent, over 44 months, with 44 trades. The test used a daily reconciliation with a maximum error of 3.637978807091713e-12, at an initial investment of 10000 units. 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 test was used as a diagnostic instrument, and not to promote a winning strategy. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and the test results can be found in research/test_vm.py/results.json.
This is a report on the VM9110600 EFA adapter train slip5 scale1. The protocol used is Protocol2230 with corrections 2233 and 2236, and the data range is from 2019-01-02 to 2022-12-30, with calibration from 2016-18 and training from 2019-22, and a holdout period until August 26, 2023. The monthly entry is based on prior variance capped at 1, with a stop at 2 ATR and a target at 6 ATR, and an additional 4 ATR for the next quarter, with a next-day break-even point and a month-end exit. The fees are 1 basis point per side, with an additional 5 basis points for slippage, and a cash yield of 0. The return is -9.791543497569688 percent, with a drawdown of 20.83371906457947 percent, a win rate of 43.75 percent, and 48 trades over 48 months. The vectorbt daily reconciliation has a maximum error of 3.637978807091713e-12 at 10000 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 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 EFA constant strategy. The test used data 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 was rebalanced on the first monthly open and had a constant calibration exposure, with terminal liquidation. The test had a position size of N1 and a monthly win frequency that was calculated separately. The fees were 1 basis point per side, with a 15 basis point slip, and a cash yield of 0. The return was 68.89241210000993 percent, with a drawdown of 12.481923756298885 percent, and a win rate of 100 percent over 44 months. The test used daily reconciliation with a maximum error of 0 at 10,000 initial units. 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 data source is https://runway.adityaperswal.workers.dev/data/stock-bars and the research results are from research/test_vm.py/results.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 a training period from 2019 to 2022, and a holdout period from 2023 to August 26. The strategy involved a monthly rebalance with constant calibration exposure and terminal liquidation, which means selling all assets at the end of the period. The position size was determined by N1, and the monthly win frequency was calculated separately. The fees were 1 basis point, or 0.01%, per side, with an additional 15 basis points, or 0.15%, slip, and a cash yield of 0%. The return was 24.21% with a drawdown of 30.36%, and the win rate was 100% over 48 months. The backtest was run with daily reconciliation and a maximum error of 0 at 10,000 initial units. There was no validation for 15-minute intervals or whole-share trading, and the data 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 results can be found in research/test_vm.py/results.json.
This is a report on a test of the VM9110600 EFA constant strategy. The test used data 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 was rebalanced on the first monthly open and had a constant calibration exposure, with terminal liquidation. The test found a return of 69.20518637653915% and a drawdown of 12.481738926862441%, with a win frequency of 100% over 44 months. The test used a daily reconciliation with a maximum error of 0 at 10,000 initial units. Fees were 1 basis point per side, with a 5 basis point slip, and cash had a 0% yield. The test did not include 15-minute or whole-share validation, and used a dividend-adjusted proxy with survivorship bias, but did not touch the global history. This was a diagnostic instrument and did not promote a winning strategy. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars and the results can be found in research/test_vm.py/results.json.
A news scan found the text of a report on an automated trading fund manager's notes for C91106 SOL. The report discusses the fund's hourly frozen net replication and cost stress. The fund uses a premise with a frozen rolling 480 hour high and 240 hour low, and an ATR14 reconstructed from full 24 hour UTC days. The fund has a 2 ATR risk, cost-aware 3R target, 20 day timeout, and a peak 2 ATR trailing target. The report states that signals occur after a completed bar, at the next exact open, and exits are sampled at hourly opens. The fund credits a 25 basis point fee per side and 10 basis point lippage baseline, with a 20 basis point stress that freezes the baseline target. The report also discusses the fund's training and holdout returns, with a training return of -0.4796588637% and a holdout return of 1.1724554945%. The report concludes with a research rejection due to negative hourly training, stress, and prior 15 minute execution test results, resulting in no order. The report cites sources https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and notes observations 24627-24674 and specification 2231.
A news scan found the text of a daily report from an automated trading fund manager for SOL. The report has three premises. Premise 1 describes the trading strategy, which uses a 20-day high and low, a 14-day average true range, or ATR, which is a measure of price volatility, and a 3R target, where R is a risk unit. The strategy also includes a 20-day timeout and a 2R cost, with a peak 2ATR trailing stop. The target is retained until the next exact open. Exits are sampled at daily opens, and missing opens are skipped. The report notes that stop fills are not guaranteed. The strategy credits a 25 basis point fee per side, which is a 0.25% fee for buying or selling, and a 10 basis point lippage baseline, which is a 0.10% expected price difference. A 20 basis point stress freezes the baseline target. The report uses a cash-only vectorbt, which is a backtesting library, with an initial cash value of 1.0. Premise 2 compares the training period from 2024 to 2025 with a holdout period from 2026 January 1 to September 11. The training return was 29.23%, the maximum drawdown, or DD, was 27.02%, and there were 9 trades. The holdout return was -8.22%, the DD was 31.79%, and there were 6 trades, with a win rate of 16.67% and a mean return of -0.64%. The stress return was -9.31%, the static return was -8.22%, and the buy and hold, or BH, return was -18.41%. Premise 3 notes that all current frozen entries are false, and daily and hourly policies are distinct, with no equal return requirement. The report concludes that the research is rejected due to negative hourly training, negative stress, and negative prior 15-minute execution test results. The accounting is valid within assumptions, but there are limitations due to unknown historical bid-ask spreads, 2025 bar anomalies, small sample size, and off-run exact trail implementation. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The observations are from 24627 to 24674, and the specification is 2231.
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 a training period from 2019 to 2022, and a holdout period from 2023 to August 26. The strategy involved rebalancing on the first monthly open with constant calibration exposure and terminal liquidation. The position size was N1, with monthly win frequency calculated separately. Fees were 1 basis point per side, with a 5 basis point slip, and cash had a 0 yield. The return was 24.453406075542116 percent, with a drawdown of 30.354470021404133 percent, and a win rate of 100 percent over 48 months. The vectorbt daily reconciliation had 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, with a proxy for survivorship, and 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 results can be found in research/test_vm.py/results.json.
A news scan found the report of an automated trading fund manager. The report discusses the performance of an Ethereum trading strategy. The strategy uses a 480-hour high and 240-hour low, with a 14-day average true range, or ATR, which is a measure of price volatility. The strategy aims to make a 3R return, which means making three times the risk amount, with a 20-day timeout and a 2R cost basis, or breakeven, and a peak 2ATR trailing stop. The strategy enters trades at the next hourly open after a signal, and exits are sampled at hourly opens, with missing opens skipped and no guaranteed stop fills. The strategy credits a 25 basis point fee per side, which is a 0.25% fee, and 10 basis points of lippage, which is a measure of the difference between the expected and actual price of a trade, as a baseline, with a 20 basis point stress test that freezes the baseline target. The strategy uses a cash-only vector and initializes with a cash amount of 1.0, with raw wealth and closed trade fee denominators reconciled to less than 1e-10 across 48 cells, with a maximum of 2.45e-15. The report compares the performance of the strategy in two time periods: from 2024 to 2025, and from January 1, 2026, to September 11, 2026. The training return was 33.25%, with a maximum drawdown of 39.72%, and 19 trades. The holdout return was 17.24%, with a maximum drawdown of 19.83%, and 6 trades, with a win rate of 66.67% and a mean return of 2.87%. The stress test return was 15.84%, with a static return of 17.24%, and a buy-and-hold return of -17.60%. The report notes that all current frozen entries are false, and that daily and hourly policies are distinct, with no equal return requirement. The report reproduces a prior return of 17.24% with six trades exactly, but notes that repeated calculation is not new independent proof. The report concludes that it is for research watch only, and no order should be placed. The report notes that the accounting is valid within assumptions, but that unknown historical bid-ask spreads, 2025 bar anomalies, small sample size, and off-run exact trail implementation limit the soundness of the results. The report cites two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The report is based on observations from 24627 to 24674, with a specification of 2231.
A news scan found the report of an automated trading fund manager for C91106 ETH. The report discusses the daily frozen net 3R replication and cost stress. The premise uses a native daily high and low with a 14-day average true range, or ATR, which is a measure of volatility. The cost-aware target has a 20-day timeout and a 2R cost breakeven, or BE, with a peak 2ATR trailing stop. The target is retained and the signal is given after the bar is completed, with the next exact open. Exits are sampled at daily opens, and missing opens are skipped, with no guaranteed stop fills. The credited asset has a 25 basis point fee per side, which is a measure of the cost, and a 10 basis point lippage baseline, which is a measure of the difference between the expected and actual price. A 20 basis point stress freezes the baseline target. The cash-only vector backtest, or bt, has an initial cash of 1.0, and the raw wealth and closed trade fee denominators are reconciled to be less than or equal to 1e-10 across 48 cells, with a maximum of 2.45e-15. The training period is from 2024 to 2025, with a holdout period from 2026 January 1 to September 11 00 UTC. The training return is 2.0291530569%, with a drawdown, or DD, of 34.9426662999%, and 15 trades. The holdout return is -17.6324659487%, with a DD of 27.3536412981%, a win rate of 20%, and 5 trades, with a mean return of -3.5218771014%. The stress return is -18.452038509%, the static return is -17.6324659487%, and the buy and hold, or BH, return is -16.7705527594%. All current frozen entries are false, and daily and hourly policies are distinct, with no equal return requirement. This report is for research watch only, and no order should be placed. The accounting is valid within assumptions, but there are limitations due to unknown historical bid ask, 2025 bar anomalies, small sample size, and off-run exact trail implementation. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The observations are from 24627 to 24674, and the specification is 2231.
A news scan found the report: Premise 1 uses a frozen rolling 480 hour high and 240 hour low, with a 14-day Average True Range (ATR), which measures price volatility, reconstructed from full 24 hour UTC days. It has a 2 ATR risk, cost-aware 3R target, 20 day timeout, +2R cost breakeven (BE) and peak-2 ATR trailing, target retained. Signals occur after a completed bar, at the next exact open, and exits are sampled at hourly opens, with missing opens skipped and no guaranteed stop fills. The report credits a 25 basis point fee per side and 10 basis point lippage baseline, with a 20 basis point stress that freezes the baseline target. The vectorbt init cash is 1.0, with raw wealth and closed trade fee denominators reconciled to less than or equal to 1e-10 across 48 cells, with a maximum of 2.45e-15. Premise 2 compares the chronological 2024-25 train, with SOL available from August 26, 2024, to the reused 2026 January 1 to September 11 00 UTC holdout, with a flat reset. The training return is 20.5581742344%, with a drawdown (DD) of 28.4815768457% and 19 trades. The holdout return is 7.6357530493%, with a DD of 12.8707598205%, a win rate of 50%, 6 trades, and a mean of 1.4019896435%. The stress is 6.3518399027%, the static is 7.6357530493%, and the buy and hold (BH) is -12.2163852845%. Premise 3 states that all current frozen entries are false, with daily and hourly distinct policies and no equal return requirement. The conclusion is for research watch only, with no order. The report notes valid accounting within assumptions, but unknown historical bid ask, 2025 bar anomalies, small N, and off-run exact trail implementation limit soundness. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, with observations from 24627 to 24674 and specification 2231.