A preregistered note was made before prices were inspected. The issuer's archive was recovered, showing actual release dates from 2021 to May 2026, along with prior scheduling notices, each recorded with a URL. The system requires a notice to be published on an earlier calendar day than the entry, and a call-only notice matched the actual release date, with no assumed first-news minute. The system enters at T-10 open and exits at T-1 close on the SPY session calendar, never on an event day. Three training events were excluded due to same-day notice, and there were zero missing-bar exclusions. The training period from 2021 to 2024 showed a return of 39.38577%, a drawdown of 14.29358%, and a win rate of 61.53846%. The holdout period from 2025 to May 2026 showed a return of -54.89695%, a drawdown of 56.61701%, and a win rate of 16.66667%, with a mean return of -10.97667% and a win-to-loss ratio of 19.97380 to 17.16676%. The system uses vectorbt open and close two-observation sessions, with all-in notional reinvestment, a fee of 5 basis points, and a slip of 10 basis points each side. The drawdown is measured at opens and closes, not at the true intraday worst. A higher slip of 25 basis points resulted in a holdout return of -55.70154%. A slip of $0.005 or $0.01 per side separately resulted in returns of -56.12889% and -57.83791%. A fixed 5-session diagnostic holdout resulted in a return of -20.19879%, while a 15-session holdout had no training and one holdout with a return of 23.41144%, but this was deemed unusable due to calendar selection. No strategy was selected from the inspected sweep. One pre-announcement in 2022 was tagged, but it was not fresh economic news. The current T-10 entry has already passed, and it is not possible to turn a late entry into a tested rule. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars and https://ir.hain.com/financial-information/quarterly-results. Note that IEX partial venue and hypothetical costs do not establish executable fills, and corporate-action mapping and all unscheduled pre-announcements were not exhaustively audited. The system rejects the long rule, but this is not a short recommendation.
A frozen protocol note, number 358, was made. This note looks at a time period from August 2021 to August 2026, which is the same market period that was previously inspected in other strategies. The return from open to close yesterday was negative, so the plan is to buy at today's close and sell at the following open. This signal was known before entering the trade, and it is based on a delayed price proxy, not an imbalance in signed orders. The first entry was made on January 5, 2021, and the last exit was made on August 27, 2026, with a total of 652 completed nights. There is a fee of 5 basis points and slippage of 5 basis points on each side of the trade. The vectorbt ledger, which is a long-only ledger, reconciles the independent net compounding within a very small margin of error, and it does not allow for negative cash. The drawdown includes the starting cash and fee marks, but it does not include unobserved intranight excursions. The mean payoff is -0.147999%, and the 95% mean confidence interval for a 5-trade block is -0.196250% to -0.103566%. The costs, including 0, 2, and 50 basis points, and raw price variants, are preserved in the archive, but they have not been tuned. The daily adjusted prices approximate the 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 is a diagnostic adaptation, and it is not an exact replication of the 02-03 ET futures, nor is it a deployable stop or target rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and it was generated using the study.py and diagnostics.py scripts.
A frozen protocol note, number 358, was used to analyze the SPY market from August 2021 to August 2026. This time period was previously examined 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, with 1420 nights completed. There was a fee of 5 basis points and slippage of 5 basis points for each buy and sell. The results were checked using a long-only vectorbt ledger, which showed that the net compounding was accurate to within 1e-8 and that the cash balance was never negative. The average payoff was -0.166131%. A 95% confidence interval for the mean payoff over 5 trades was between -0.197787% and -0.135059%. 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 for daily changes, but the simulation did not account for certain market factors, such as auctions or partial profits. This analysis is a diagnostic tool and does not exactly replicate the 02-03 ET futures or provide 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 scripts.
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 trading strategy was tested on the EEM stock using a simple moving average (SMA) crossover rule, which is a method to determine when to buy or sell a stock based on its average price over a certain period. The strategy was based on three premises: signals to buy or sell must happen before the actual buy or sell, costs and out-of-sample data must be clearly stated, and profits from stocks do not necessarily mean profits from options. The rule was to buy when the 10-day SMA was higher than the 50-day SMA and sell when it was lower. The test started with $1000 in cash in January 2026 and used data from 2022 to 2025 to train the strategy. The results from 2022 to 2025 showed a return of -9.63% and a maximum loss of 27.75%. The test from 2026 to September 4 showed a return of 9.70% and a maximum loss of 10.74%. The strategy was tested on 1173 daily bars of data from the IEX feed. The results are limited by the small sample size and the fact that the strategy was only tested on a few ETFs. The test also did not include any options data, so no option allocation was made. A news scan found the headline was not applicable in this case, as this is a detailed report of a trading strategy test. The strategy was implemented using vectorbt and the results were also run using a command-line interface (CLI) with the same parameters.
This is a report on a trading strategy for the XLF stock. The strategy is based on two moving averages, a short-term one and a long-term one. The short-term average is called SMA10 and the long-term average is called SMA50, which is a type of Simple Moving Average, a calculation that shows the average price of a stock over a certain period. The strategy buys the stock when the short-term average is higher than the long-term average and sells when it is lower. The report is based on data from 2022 to 2026. From 2022 to 2025, the strategy made a return of 26.59% with a maximum loss of 15.75%. From 2026 to September 4, the return was 7.57% with a maximum loss of 8.40%. The strategy was tested on 1173 days of data. The results are limited because the test was only done on a small number of days and only on one type of stock data. A news scan found the data source to be https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy was implemented using a tool called vectorbt and a command line interface. The command line interface was run with the following flags: --symbols XLF,XLE,GLD,EEM,IWM,TLT --strategy sma-cross --fast10 --slow50 --days1709. The source code was inspected at /app/agent/src/backtest/bars.ts. The results cannot be confirmed because the command line interface does not include an adjustment for the data. The report notes that a small test does not mean that there is no risk of loss. There is also no data on option returns, so no options were allocated.
A trading strategy was tested on the XLE stock, which is an exchange-traded fund, or ETF, that tracks the energy sector. The strategy was based on two moving averages, a short-term one, called SMA10, and a long-term one, called SMA50, which are like trends that smooth out price changes over 10 and 50 days, respectively. The rule was to buy XLE when the short-term trend was above the long-term trend and sell when it was not. The test started with $1000 in cash in January 2026 and used data from 2022 to 2025 to train the strategy. The results from 2022 to 2025 were a return of -31.95% and a maximum loss of 49.04%. The results from January 2026 to September 4, 2026, were a return of 27.71% and a maximum loss of 17.27%. The strategy was tested on 1173 days of data from the IEX stock exchange. The test also used a programming library called vectorbt. The results are limited because the test was only done on a few stocks and did not include options trading. The test also did not account for all possible risks and costs, like fees and slippage, which is when the price of a stock changes quickly. The results should not be used to make investment decisions without considering these limitations.
A trading fund manager made decisions based on certain premises. The premises are: 1. Signals must come before trades are made. 2. Costs and times when the system is not used must be clearly stated. 3. Making money from stocks does not mean making money from options. The manager used a rule that says if the average closing price over 10 days is higher than the average closing price over 50 days, then buy the next day and sell the day after that. The manager started with $1000 in January 2026, used past data to prepare the indicators, and closed the trade at the last bar. The manager paid fees and slippage of 0.05% each time, and also considered higher stress levels of 0.15%. The manager tested the system on the TLT symbol, which is a type of bond fund, from 2022 to 2025 and got a return of -2.998%, a maximum loss of 13.821%, and won 33.333% of the time. From 2026 to September 4, the return was -3.911%, the maximum loss was 5.158%, and there were no winning trades. The manager used 1173 daily bars of data from the website https://runway.adityaperswal.workers.dev/data/stock-bars, which provides stock market data. The manager also used a system called vectorbt and ran a command line interface with certain flags, but the CLI did not include adjustments, so the results cannot be directly compared. The manager notes that the small number of trades, the selection of certain ETFs, and the limited data source may limit the conclusions that can be drawn. Additionally, not seeing a loss in a small sample does not mean that losses are impossible. The manager did not consider option returns or executable quotes, so did not allocate any funds to options.