A frozen protocol note, number 358, was used for training from 2016 to 2020. The strategy involved buying the SPY at the close of the market on days when the open-to-close return was negative, and then selling it at the next day's open. This signal was known before entering the trade, and it was based on a delayed price proxy, not an order imbalance. The first entry was on January 6, 2016, and the last exit was on December 31, 2020, with a total of 566 completed nights. The fee was 5 basis points, and slippage was also 5 basis points, on each side of the trade. The results were calculated using a long-only vector, and the net compounding was within 1e-8, with nonnegative cash. The drawdown included starting cash and fee marks, but did not include unobserved intranight excursions. The mean payoff was -0.173098%, with a 95% confidence interval of -0.266500% to -0.100093%. The costs were 0, 2, and 50 basis points, and raw price variants were preserved in the archive, but not tuned. Daily adjusted prices were used to approximate distributions and endpoints, but the market or limit venue cannot guarantee auctions, and there was no 15-minute exit or partial profit simulation in this backtest. This is a diagnostic adaptation, and not an exact replication of the 02-03 ET futures, and not a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used to train a trading system from 2016 to 2020. The system was designed to buy at the close of each session and sell at the following open. It made its first entry on 2016-01-04 and its last exit on 2020-12-31, completing 1258 nights of trading. The system had a fee of 5 basis points and slippage of 5 basis points on each side of the trade. A long-only vectorbt ledger was used to record the buys and sells, and it showed that the system's net worth grew independently, with no negative cash balances, and was accurate to within 1e-8. The system's mean payoff was -0.157212%. A 95% confidence interval for the mean payoff over 5-trade blocks was -0.206346% to -0.112683%. The system preserved three variants of costs, 0, 2, and 50 basis points, and raw price variants, but did not tune them. The daily adjusted prices used in the system were approximate and did not guarantee exact market or limit venue auctions. The system did not simulate 15-minute exits or partial profits. This is a diagnostic adaptation and not an exact replication of the 02-03 ET futures or a deployable stop/target rule. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used to analyze the IWM market from August 2021 to August 2026, a period previously examined in other strategies. The analysis found that when the market had a negative return from open to close the previous day, it was a signal to buy at the close of the market today and sell at the open of the market the next day. This signal was known before entering the trade, and it was based on a delayed price proxy, not an order imbalance, which refers to a difference between the number of buy and sell orders. The first entry was made on January 4, 2021, and the last exit was on August 27, 2026, with a total of 711 completed trades at night. Each trade had a fee of 5 basis points, or 0.05%, and a slippage of 5 basis points, which is the difference between the expected price and the actual price. The analysis used a long-only strategy, meaning only buys were made, and the results were calculated using a vectorbt ledger, which is a tool used to track and analyze trades. The mean payoff was -0.139892%, and the 95% confidence interval for the mean payoff was between -0.196869% and -0.082145%. The analysis also preserved different variants of the data, including costs of 0, 2, and 50 basis points, but these were not used to adjust the results. The daily adjusted prices used in the analysis were approximate and did not take into account certain market factors, such as auctions. This analysis is a diagnostic tool and not a exact replication of a specific trading strategy, and it is not a deployable stop or target rule. The data used in the analysis came from https://runway.adityaperswal.workers.dev/data/stock-bars and was analyzed using the study.py and diagnostics.py scripts.
A frozen protocol note, number 358, was used to analyze the IWM overnight trading strategy from August 2021 to August 2026. This time period was previously examined in other strategies. The strategy involved buying at the close of each session and selling at the open of the next session. The first entry was on January 4, 2021, and the last exit was on August 31, 2026, with a total of 1420 completed nights. The fee was 5 basis points and slippage was 5 basis points on each side. The results were calculated using a long-only vectorbt staged buy/sell ledger, which reconciles independent net compounding within a small margin of error, and includes nonnegative cash. The mean payoff was -0.145874%. A 95% confidence interval for the mean payoff over 5-trade blocks was -0.189569% to -0.102042%. The backtest preserved variants with costs of 0, 2, and 50 basis points, as well as raw price variants, but these were not tuned. Daily adjusted prices were used to approximate distributions and endpoints. Note that this backtest did not simulate auctions, 15-minute exits, or partial profit taking, and is a diagnostic adaptation, not an exact replication of 02-03 ET futures or a deployable stop/target rule. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was conducted using study.py and diagnostics.py.
A frozen protocol note, number 358, was used for training from 2016 to 2020. The strategy involved buying at the close and selling at the next open when the previous day's return was negative. This signal was known before entering the trade, but the price used was delayed and did not account for order imbalance. The first entry was on 2016-01-05 and the last exit was on 2020-12-31, with a total of 602 completed nights. The fee was 5 basis points and slippage was also 5 basis points on each side of the trade. The results were calculated using a long-only vectorbt ledger, which reconciles the net compounding within a small margin of error, and includes starting cash and fees but excludes unobserved price movements during the night. The mean payoff was -0.180225%, with a 95% confidence interval of -0.269987% to -0.105063% over 5-trade blocks. Different cost variants were preserved but not used for tuning. The daily adjusted prices used in the study approximate real market distributions and endpoints, but do not guarantee auction prices and do not include 15-minute exits or partial profit simulations. This study is a diagnostic adaptation and not an exact replication of futures trading from 02-03 ET, and it is not a deployable stop or target rule. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was conducted using study.py and diagnostics.py.
A frozen protocol note, number 358, was used to train a system from 2016 to 2020. The system was designed to buy at the close of each session and sell at the following open. It started on January 4, 2016, and ended on December 31, 2020, completing 1258 nights. The system had a fee of 5 basis points and slippage of 5 basis points on each buy and sell. A long-only strategy was used, meaning the system only bought and never sold short. The system's performance was calculated using a method called vectorbt, which is a way to backtest trading strategies. The results showed a mean payoff of -0.133879 percent. A 95 percent confidence interval, which is a way to measure the reliability of the results, showed a range of -0.186687 to -0.082900 percent. The system was tested with different cost scenarios, including 0, 2, and 50 basis points. The prices used were adjusted daily and were approximate. The system was not designed to exactly replicate real-time trading and did not include features like stop-loss or take-profit rules. The data used for the study came from https://runway.adityaperswal.workers.dev/data/stock-bars and was analyzed using two scripts called study.py and diagnostics.py.
A frozen protocol note, number 358, was used to analyze the EFA market from August 2021 to August 2026, a period previously examined in other strategies. The analysis found that when the market had a negative return from open to close the previous day, it was a signal to buy at the close of the market today and sell at the open of the market the next day. This signal was known before entering the trade and was based on a delayed price proxy, not an order imbalance that was not signed. The first entry into the market was on January 4, 2021, and the last exit was on August 31, 2026, with 631 completed nights. The fee was 5 basis points and the slippage was also 5 basis points for each side of the trade. The analysis used a long-only vectorbt staged buy and sell ledger, which reconciles independent net compounding within a very small margin of error, and does not allow for negative cash. The drawdown includes the starting cash and fee marks, but does not include unobserved intranight excursions. The mean payoff was -0.240984 percent, with a 95 percent confidence interval of -0.309603 percent to -0.175063 percent for a 5-trade block. The costs of 0, 2, and 50 basis points, as well as raw price variants, were preserved in the archive but not tuned. The daily adjusted prices approximate distributions and endpoints, but the market or limit venue cannot guarantee auctions, and there is no 15-minute exit or partial profit simulation in this backtest. This analysis is a diagnostic adaptation and not an exact replication of the 02-03 ET futures, and it is not a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used to analyze the EFA market from August 2021 to August 2026, which is the same period previously inspected in other strategies. The plan was to buy at the close of each session and sell at the following open. The first buy was on January 4, 2021, and the last sell was on August 31, 2026, resulting in 1420 completed nights. The cost was 5 basis points for the fee and 5 basis points for slippage on each side, which is 10 basis points per side. A long-only strategy was used, which means only buying was allowed, and the results were checked for accuracy. The average payoff was -0.197311 percent, and the 95 percent confidence interval for the mean of 5-trade blocks was -0.238236 percent to -0.157933 percent. Different cost scenarios, including 0, 2, and 50 basis points, were preserved in the archive but not adjusted for optimal performance. The prices used were adjusted daily and approximate the actual distributions and endpoints. However, the market or limit venue cannot guarantee auctions, and this backtest did not simulate 15-minute exits or partial profits. This analysis is a diagnostic adaptation and not an exact replication of the 02-03 ET futures or a deployable stop/target rule. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used for training from 2016 to 2020. The strategy was to buy at the close of the day if the open-to-close return was negative, and sell at the following open. This signal was known before entering the trade, and it used a delayed price proxy, not an order imbalance. The first entry was on 2016-01-08 and the last exit was on 2020-12-31, with 559 completed nights. The fee was 5 basis points and slippage was 5 basis points on each side. A long-only vector was used to stage buy and sell transactions, and the ledger was reconciled to ensure independent net compounding within a small margin of error, with nonnegative cash. The drawdown included starting cash and fee marks, but did not include unobserved intranight excursions. The mean payoff was -0.216290%, with a 95% confidence interval of -0.318499% to -0.127472%. Different cost variants were preserved in the archive, but not tuned. Daily adjusted prices were used to approximate distributions and endpoints, but the market or limit venue cannot guarantee auctions, and there was no 15-minute exit or partial profit simulation in this backtest. This is a diagnostic adaptation, not an exact replication of the 02-03 ET futures, and not a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used to train a trading system from 2016 to 2020. The system was set to buy at the close of each session and sell at the following open. It made its first trade on January 4, 2016, and its last trade on December 31, 2020, completing 1258 trades overnight. The system was charged a fee of 5 basis points and allowed for 5 basis points of slippage on each trade. The results were calculated using a long-only strategy, which means the system only bought and never sold short, and the calculations were checked to make sure they were accurate to within a very small margin of error, known as 1e-8. The system's average profit was -0.178825 percent, meaning it lost value. A 95 percent confidence interval, which is a way to measure the reliability of the results, showed that the average profit was likely between -0.231093 percent and -0.127730 percent. The system was tested with different costs, including 0, 2, and 50 basis points, and the results were saved. The prices used were adjusted daily and were approximate. The system did not simulate real-world market conditions, such as auctions or partial profits. This test was used to diagnose issues and is not a exact replica of a real-world trading system, specifically the 02-03 ET futures replication, and should not be used as a rule for stopping or targeting trades. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and was analyzed using study.py and diagnostics.py scripts.
A frozen protocol note, number 358, was used to analyze the EEM market from August 2021 to August 2026, which is the same period previously examined in other strategies. The analysis found that when the market had a negative return from open to close the previous day, the strategy was to buy at the close of the market today and sell at the open of the market the next day. This signal was known before entering the trade, and it was based on a delayed price proxy, not an order imbalance that was not signed. The first entry into the market was on January 4, 2021, and the last exit was on August 28, 2026, with a total of 653 completed nights. The fee was 5 basis points and the slippage was also 5 basis points for each side of the trade. The analysis used a long-only vectorbt staged buy and sell ledger, which reconciles the independent net compounding within a very small margin of error, and the cash was always nonnegative. The drawdown included the starting cash and fee marks, but did not include any unobserved intranight excursions. The mean payoff was -0.239197%, and the 95% mean confidence interval for a 5-trade block was -0.311146% to -0.167636%. The analysis preserved the costs of 0, 2, and 50 basis points, as well as the raw price variants, in the archive, but did not tune them. The daily adjusted prices were used to approximate the distributions and endpoints, but the market or limit venue cannot guarantee auctions, and there was no 15-minute exit or partial profit simulation in this backtest. This analysis is a diagnostic adaptation and is not an exact replication of the ETF futures from 02-03 ET, and it is not a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used to analyze the EEM market from August 2021 to August 2026, which is the same period previously inspected in other strategies. The plan was to buy at the close of each session and sell at the following open. The first buy was on January 4, 2021, and the last sell was on August 31, 2026, resulting in 1420 completed nights. The fee was 5 basis points and slippage was 5 basis points for each buy and sell. The analysis used a long-only strategy and reconciled the buy and sell ledger to ensure accurate net compounding within a small margin of error, 1e-8, and nonnegative cash. The drawdown calculation included starting cash and fee marks but did not include unobserved price changes during the night. The mean payoff was -0.195460%. A 95% confidence interval for the mean payoff over 5-trade blocks was -0.247297% to -0.145154%. The analysis preserved three variants of costs, 0, 2, and 50 basis points, and raw price variants in the archive, but did not tune them. Daily adjusted prices were used to approximate distributions and endpoints. The market or limit venue cannot guarantee auctions, and this backtest did not simulate 15-minute exits or partial profits. This analysis is a diagnostic adaptation and not an exact replication of the 02-03 ET futures or a deployable stop/target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used for training from 2016 to 2020. The strategy involved buying at the close of a day when the open-to-close return was negative and selling at the following open. This signal was known before entering the trade, and it was based on a delayed price proxy, not an order imbalance that was not signed. The first entry was on 2016-01-05 and the last exit was on 2020-12-30, with a total of 578 completed nights. A fee of 5 basis points and slippage of 5 basis points were applied to each side of the trade. The results were calculated using a long-only vector, with a buy and sell ledger that was reconciled independently to ensure non-negative cash and net compounding within a small margin of error, 1e-8. The mean payoff was -0.159961%, with a 95% confidence interval of -0.246600% to -0.074461%. Different cost variants, 0, 2, and 50 basis points, and raw price variants were preserved in the archive but not tuned. Daily adjusted prices were used to approximate distributions and endpoints, but the market or limit venue cannot guarantee auctions, and there was no 15-minute exit or partial profit simulation in this backtest. This is a diagnostic adaptation and not an exact replication of the 02-03 ET futures or a deployable stop/target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used for training from 2016 to 2020. The strategy was to buy at the close of each session and sell at the following open. The first trade was on January 4, 2016, and the last trade was on December 31, 2020, with a total of 1258 completed trades overnight. The fee was 5 basis points and slippage was 5 basis points for each buy and sell. The results were calculated using a long-only strategy, with a ledger that reconciles the buys and sells, and the net compounding was calculated independently, with a maximum error of 1e-8, and the cash was always nonnegative. The drawdown, or loss, includes the starting cash and fees, but does not include any potential losses that may have occurred during the night. The mean payoff, or average return, was -0.152876%. A 95% confidence interval for the mean payoff over 5 trades was between -0.210277% and -0.096504%. The costs were 0, 2, and 50 basis points, and the raw price variants were preserved in the archive, but not used to adjust the strategy. The daily adjusted prices were used to approximate the distributions and endpoints, but the market or limit venue cannot guarantee auctions, and there was no simulation of 15-minute exits or partial profits in this backtest. This is a diagnostic adaptation, not an exact replication of the ETF futures from 02-03 ET, and not a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was done using study.py and diagnostics.py.
A frozen protocol note, number 358, was used to analyze the QQQ market from August 2021 to August 2026, a period previously examined in other strategies. The analysis found that when the market had a negative return from open to close the previous day, it was a signal to buy at the close and sell at the following open. This signal was known before entering the trade and was based on a delayed price proxy, not an order imbalance, which refers to a difference in the number of buy and sell orders. The first entry was on January 5, 2021, and the last exit was on August 27, 2026, with 658 completed trades at night. The fee was 5 basis points, or 0.05%, and slippage, which is the difference between expected and actual trade prices, was also 5 basis points per trade. The analysis used a long-only strategy, meaning only buys were considered, and a vectorbt ledger, which is a tool used to track and analyze trades. The results showed a mean payoff of -0.136809%, and a 95% confidence interval of -0.197342% to -0.078657%. The analysis preserved different cost variants, but did not tune them. The daily adjusted prices used in the analysis were approximate and did not guarantee exact market or limit prices. This analysis is a diagnostic tool and not a replication of exact trading results or a deployable stop-loss rule. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis used study.py and diagnostics.py scripts.
A frozen protocol note, number 358, was used to analyze the QQQ market from August 2021 to August 2026, which is the same period previously inspected in other strategies. The plan was to buy at the close of each session and sell at the following open. The first buy was on January 4, 2021, and the last sell was on August 31, 2026, resulting in 1420 completed overnight trades. The cost was 5 basis points for the fee and 5 basis points for slippage on each buy and sell. The analysis used a long-only strategy and ensured that the results were accurate to within a very small margin, 1e-8, and that the cash balance was never negative. The average profit per trade was -0.156518 percent, and the 95 percent confidence interval for the average profit over 5 trades was between -0.197996 percent and -0.115258 percent. Different cost scenarios, 0, 2, and 50 basis points, were preserved in the archive but not adjusted for optimal performance. The prices used were adjusted for daily changes and approximate the actual market conditions, but the analysis did not account for certain market factors like auctions and did not simulate exiting trades within 15 minutes or taking partial profits. This analysis is a diagnostic tool and not an exact replication of a specific trading strategy or a set of rules that can be used for actual trading. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis was done using the study.py and diagnostics.py programs.
A frozen protocol note, number 358, was used for training from 2016 to 2020. The strategy involved buying at the close of the market and selling at the next day's open if the previous day's return was negative. This signal was known before entering the trade, but the price used was delayed and did not account for order imbalances. The first entry was on January 6, 2016, and the last exit was on December 31, 2020, with a total of 560 completed nights. The fee was 5 basis points and slippage was also 5 basis points for each side of the trade. The results were calculated using a long-only strategy and reconciled to ensure accuracy within a very small margin of error, and the drawdown included the starting cash and fees but did not account for price changes during the night that were not observed. The mean payoff was -0.142774%, and the 95% confidence interval for the mean payoff over 5-trade blocks was -0.228171% to -0.067808%. Different cost scenarios, including 0, 2, and 50 basis points, were preserved in the archive but not used to adjust the results. The daily adjusted prices used in the study approximated the actual distributions and endpoints, but the market and limit venue used in the simulation could not guarantee auctions and did not include 15-minute exits or partial profit simulations. This study was a diagnostic adaptation and not an exact replication of the QQQ ETF futures from 02-03 ET, and it is not a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the study was conducted using the study.py and diagnostics.py scripts.
A frozen protocol note, number 358, was used to train a system from 2016 to 2020. The system was set to automatically buy at the close of each session and sell at the following open. It started on January 4, 2016, and ended on December 31, 2020, completing 1258 nights. The system had a fee of 5 basis points and a slippage of 5 basis points on each buy and sell. A long-only system, which means it only buys and never sells short, was used and the results were checked to make sure they were accurate to within a very small margin of error, known as 1e-8. The system's performance was measured, including the effect of fees, but not including any potential losses that might have happened during the night when the system was not actively trading. The average payoff was -0.140482 percent. A 95 percent confidence interval, which is a way of measuring the reliability of the results, was calculated over 5 trade blocks and found to be between -0.185974 percent and -0.096823 percent. Different versions of the system, with costs of 0, 2, and 50 basis points, were preserved but not used to adjust the results. The prices used were adjusted daily to try to match real market conditions, but the system did not try to exactly replicate real market conditions, such as auctions, and did not test exiting trades after 15 minutes or taking partial profits. This was a test to help understand how the system works and is not a real trading strategy. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and was analyzed using study.py and diagnostics.py.
A frozen protocol note, number 294, was used to analyze data from January 2016 to December 2021, with a holdout period from January 2022 to August 2026. The analysis started with the first session of each quarter, after the prior quarter's close, and used a stop loss and target based on the prior 20 sessions' average true range, or ATR, which is a measure of price volatility. The stop loss was set at 1 times the ATR and the target was set at 1.5 times the ATR, with a maximum of 20 sessions before moving on to the next open, and no reentry was allowed in the same quarter. The analysis was long-only, meaning it only considered buying, and used cash-only transactions. The results were calculated using vectorbt, a backtesting library, and showed a net mean return of -0.009944%, an average win of 0.814326%, and an average loss of 0.609413%, with a quarter Kelly criterion of 0.000000, which is a measure of the optimal fraction of capital to allocate to a trade. The drawdown, or peak-to-trough decline, included the initial cash and session-ending marks. The analysis used daily adjusted bars, which approximate the distribution of trades and fills, but did not validate sparse trading or 15-minute stop and partial-profit execution. This analysis is an adaptation of an exit strategy, not the exact original trading strategy from the paper. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files study.py, trades.json, and metrics.json were used.
A frozen protocol note, number 294, was used to analyze data from January 2016 to December 2021, with a holdout period from January 2022 to August 2026. The analysis started with the first session of each quarter, after the previous quarter's close, and used a stop loss and target based on the average true range, or ATR, of the previous 20 sessions. The ATR is a measure of how much a stock's price moves. The stop loss was set at 1 times the ATR and the target was set at 1.5 times the ATR, with a maximum of 20 sessions. If the target or stop loss was not reached within 20 sessions, the position was closed at the next open. There was no reentry into the trade during the same quarter. The analysis was long-only, meaning only buys were considered, and used cash-only transactions. The results were checked using two different methods, vectorbt order ledger and independent compounded trade returns, which agreed with each other to within a very small margin of 1e-8. The fees were 5 basis points, or 0.05%, plus slippage of 45 basis points, or 0.45%, each way. A basis point is a unit of measurement for interest rates and fees. The analysis used gap stops at the open and ambiguous daily bars stopped at the first occurrence. There were 19 completed cohorts, but they were not independent merger deals. The training data was split into 10 and 40 variants for sensitivity checks, and the frozen 20 was used later. The results showed a net mean return of -0.965262%, an average win of 0.115454%, and an average loss of 1.092405%. The quarter Kelly, a measure of the optimal fraction of capital to allocate to a trade, was 0.000000. The drawdown, or peak-to-trough decline, included the initial cash and session-ending marks. The daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution was validated. This analysis was an exit adaptation, not an exact replication of the original paper's trading strategy. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files study.py, trades.json, and metrics.json were used.
A frozen protocol note, number 294, was used to analyze data from January 2016 to December 2021, as data from 2015 was not available. The analysis also included a holdout period from January 2022 to August 2026. The strategy involved entering a position on the first session of each quarter, after the prior quarter's close, and setting a stop loss and target based on the prior 20-session average true range, or ATR, which is a measure of price volatility. The position was held for a maximum of 20 sessions, and then the next open, with no reentry allowed that quarter. The analysis was long-only, meaning it only considered buying, and used cash-only transactions. The results were verified using two different methods, vectorbt order ledger and independent compounded trade returns, which agreed within a very small margin of 1e-8. The fees were 5 basis points, or 0.05%, plus 10 basis points of slippage, which is the difference between the expected and actual price of a trade, each way. The analysis also included gap stops at the open, and ambiguous daily bars were stopped first. There were 19 completed cohorts, but these were not independent merger deals, and no unfinished endpoints were counted. The training data included 10 and 40 variants, which were sensitivity checks, and the frozen 20 was used later. The net mean return was -0.269581%, the average win was 0.552549%, and the average loss was 0.867493%. The quarter Kelly, a measure of the optimal fraction of capital to allocate to a trade, was 0.000000. The drawdown, or peak-to-trough decline, included initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop/partial-profit execution was validated. This analysis was an exit adaptation, not an exact replication of the original paper's trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were also used.
A frozen protocol note, number 294, was used for training. The actual training data was from January 2016 to December 2021, because data from 2015 was not available. The holdout period was from January 2022 to August 2026. The training started with the first session open after the prior quarter closed. A stop loss, called stop1ATR, and a target, called target1.5ATR, were set based on the average true range, or ATR, of the prior 20 sessions. The maximum number of sessions was 40, then the next open was used. There was no reentry in the same quarter. The training was long-only, meaning only buys were considered, and cash-only, meaning all trades were paid in cash. The vectorbt order ledger and the independent compounded trade returns agreed within a very small margin, 1e-8. The fee was 5 basis points, or bp, plus slippage of 10bp each way. A basis point is one hundredth of one percent. Gap stops were executed at the open, and ambiguous daily bars stopped first. There were 23 completed cohorts, which are groups of data, but they were not independent merger deals. No unfinished endpoints were counted. Ten out of 40 variants were used for sensitivity checks, and 20 were frozen for later use. The net mean return was 0.114560 percent, the average win was 1.332183 percent, and the average loss was 0.822073 percent. The quarter Kelly, a measure of risk, was 0.021499. Drawdown, or loss, includes the initial cash and the marks at the end of each session. Daily adjusted bars were used to approximate distributions and fills, but sparse trading and 15-minute stop or partial-profit execution were not validated. This is an adaptation of an exit strategy, not the exact original-paper trading. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files are study.py, trades.json, and metrics.json.
A frozen protocol note, number 294, was used for training. The training data was from January 2016 to December 2021, as the requested data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The training process started with the first session open after the prior quarter's close. The stop loss and target were set based on the prior 20-session average true range, denoted as ATR20, with the stop loss set at 1 times the ATR and the target set at 1.5 times the ATR. The maximum number of sessions was 20, after which the next open was used. There was no reentry allowed in the same quarter. The training was long-only, meaning only buying was allowed, and cash-only, meaning all trades were paid in cash. The vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. A fee of 5 basis points, or 0.05%, plus slippage of 10 basis points, or 0.10%, each way was applied. Gap stops were executed at the open, and ambiguous daily bars stopped first. There were 23 completed cohorts, but they were not independent merger deals. No unfinished endpoints were counted. The training included 10 out of 40 variants as sensitivity checks, and the frozen 20 were used later. The net mean return was 0.060697%, the average win was 1.208300%, and the average loss was 0.822073%. The quarter Kelly criterion was 0.012558. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution was validated. This is an adaptation of an exit strategy, not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A frozen protocol note, numbered 294, was used for training. The actual training data was from January 2016 to December 2021, as the requested data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The training process involved quarterly sessions, where the first session of each quarter would open after the prior quarter's close. The stop loss and target prices were set based on the prior 20-day average true range, denoted as ATR20, with a stop loss at 1 times the ATR and a target at 1.5 times the ATR, with a maximum of 10 sessions before the next open, and no reentry was allowed in the same quarter. The training was long-only, meaning only buy positions were considered, and cash-only, meaning all positions were closed before the next quarter. The vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. A fee of 5 basis points, or 0.05%, plus slippage of 10 basis points, or 0.10%, each way, was applied. Gap stops were executed at the open, and ambiguous daily bars stopped at the first opportunity. There were 23 completed cohorts, which are not independent merger deals, and no unfinished endpoints were counted. The training included 10 out of 40 variants as sensitivity checks, and the remaining 20 were used later. The results showed a net mean return of -0.031775%, an average win of 0.995613%, and an average loss of 0.822073%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution was validated. This is an adaptation of an exit strategy, not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A frozen protocol note, number 294, was used for actual training data from January 2016 to December 2021, as data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The strategy involved quarterly trades, with the first session open after the prior quarter's close. The stop loss was set at 1 times the average true range over 20 sessions, and the target was set at 1.5 times the average true range, with a maximum of 20 sessions before the next open, and no reentry was allowed that quarter. The strategy was long-only, and the vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. The fee was 0 basis points, plus 2 basis points of slippage each way. The strategy used gap stops at the open, and ambiguous daily bars stopped first. There were 19 completed cohorts, but these were not independent merger deals, and no unfinished endpoints were counted. The training data included 10 and 40 variants as sensitivity checks, and the frozen 20 was used later. The net mean return was -0.068713%, the average win was 0.400042%, and the average loss was 0.490593%, with a quarter Kelly of 0.000000. The drawdown included the initial cash and session-ending marks. The daily adjusted bars approximated distributions and fills, but neither sparse trading nor 15-minute stop/partial-profit execution was validated. This is an adaptation of an exit strategy, not the exact original paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.