A news scan found the following report: Premises for this stock diagnostic are that the signal at close must fill the next open, holdout results cannot be selected for an option payoff, and stops are not part of this fixed-horizon stock diagnostic. The diagnostic uses a rule called Frozen note963, which means a negative 5-day return and a price below the 50-day Simple Moving Average, or SMA, and a 20-day Realized Volatility, or RV, greater than the 60-day RV. When this rule is met, the system enters a trade at the next open and exits 10 sessions later at the open, considering the neighboring 5 and 20 sessions. The system does not allow overlapping trades or incomplete terminal trades, and it was trained on data from 2022 to 2025 and tested on data from 2026. The system uses a tool called vectorbt to explicitly consider open price, fees of 5 basis points, and slippage of 10 basis points on each side, with 1000 units of normalized all-in equity. The system marks trades to the daily close, which misses intraday drawdown. Trade count and profit, or p, include net costs, and no new stock proposal is authorized by this test. The primary train results for the stock ETHA are a return of -18.561335666821652, a drawdown of 37.46929896330313, 7 trades, a profit of 0.2857142857142857, a win of 21.461569113206465, and a loss of 10.58598380889804. The holdout results are a return of -6.68877159121663, a drawdown of 19.478436600718585, 5 trades, a profit of 0.4, a win of 6.4743335564674735, and a loss of 6.020303360610795. All variants of the system are listed, including different hold periods. The system also considers other stocks, such as IBIT, but they have shorter inception histories. The training loses or holdout uncertainty/small N prevents independent robust edge, and positive isolated variants are not adopted. The actual option holdout is 0, and this bearish stock-regime/rebound test neither prices a put nor validates a call. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was analyzed using a tool called research/analyze.py. A separate analysis of the stock XLF found a mean-reversion of gross +3.244%, a drawdown of 1.622%, 7 trades, and a win rate of 85.714% over 251 bars.
This is a report on a stock diagnostic test for IBIT. The test had three premises: 1) a signal at close must fill the next open, 2) holdout results cannot be selected for an option payoff, and 3) stops are not part of this fixed-horizon stock diagnostic. The test used a specific set of rules, including entering a trade at the next open if the 5-day return was negative and the stock was below its 50-day Simple Moving Average (SMA), and exiting 10 sessions later at the open. The test also considered the sensitivity of neighboring 5 and 20 sessions. The test was run on data from 2022 to 2025 for training and 2026 for holdout. The results showed a return of 0.5832857192442361% and a drawdown of 21.45637448712352% for the training period, and a return of -19.019164659913827% and a drawdown of 23.36184225424448% for the holdout period. The test also considered variants with different hold periods, including 5, 10, and 20 sessions. The results of these variants are listed below: a list of results for different hold periods, including return, drawdown, number of trades, and win/loss ratios. The test used a tool called vectorbt and considered fees of 5 basis points and slippage of 10 basis points on each side. The test also used a normalized all-in equity of 1000. The results are based on daily close prices and do not account for intraday drawdown. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=IBIT&timeframe=1Day&feed=sip&adjustment=all. The test was run using a script called research/analyze.py. A separate test on XLF showed a mean-reversion of 3.244% with a drawdown of 1.622% and a win rate of 85.714% over 251 bars.
A news scan found the headline '06UTC FXI fixed downside-regime stock diagnostic, 2026 holdout'. This is a report about a stock diagnostic test on the FXI stock. The test had three premises: 1) a signal at close must fill the next open, 2) holdout results cannot be selected for an option payoff, and 3) stops are not part of this fixed-horizon stock diagnostic. The test used a specific set of rules, including entering the next open and exiting 10 sessions later at open, with a neighboring 5/20 sessions sensitivity. The test was run on the FXI stock, with a training period from 2022 to 2025 and a holdout period in 2026. The results of the test are as follows: for the training period, the return was -15.081082584587307%, the drawdown was 34.42910155795327%, the number of trades was 22, and the percentage of winning trades was 0.4090909090909091. For the holdout period, the return was -8.746217322893516%, the drawdown was 16.696889109724232%, the number of trades was 7, and the percentage of winning trades was 0.2857142857142857. The test also included variants with different hold periods, including 5, 10, and 20 sessions. The results of these variants are listed. The test used a vectorbt system with explicit open price, fees of 5 basis points, and slippage of 10 basis points on each side, with a normalized all-in equity of 1000. The test did not include new stock proposals and did not validate any options. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FXI&timeframe=1Day&feed=sip&adjustment=all, and the test was run using the research/analyze.py script. A separate test on the XLF stock using a mean-reversion strategy found a gross return of 3.244%, a drawdown of 1.622%, and a win percentage of 85.714% over 251 bars.
A news scan found the headline '06UTC EWJ fixed downside-regime stock diagnostic, 2026 holdout'. This is a report about a stock diagnostic test on the EWJ stock. The test had three premises: 1) a signal at close must fill the next open, 2) holdout results cannot be selected for an option payoff, and 3) stops are not part of this fixed-horizon stock diagnostic. The test used a specific set of rules, including entering a trade at the next open if the stock had a negative 5-day return and was below its 50-day simple moving average, and exiting the trade 10 sessions later at the open. The test was run on data from 2022 to 2025, and the results were evaluated on data from 2026. The test used a vectorbt system with explicit open price, fees of 5 basis points, and slippage of 10 basis points on each side, with 1000 units of normalized all-in equity. The results of the test are as follows: for the training period, the stock had a return of -10.069275533680367%, a maximum drawdown of 21.021798073020637%, and a win rate of 0.5789473684210527, with 19 trades. For the holdout period, the stock had a return of 15.09191833882411%, a maximum drawdown of 4.9920184070356255%, and a win rate of 0.75, with 4 trades. The test also evaluated different variants of the strategy, with hold periods of 5, 10, and 20 sessions. The results of these variants are listed below. The test did not authorize any new stock proposals. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EWJ&timeframe=1Day&feed=sip&adjustment=all, and the research was conducted using the research/analyze.py script. A separate test on the XLF stock found a mean-reversion strategy with a gross return of 3.244%, a maximum drawdown of 1.622%, and a win rate of 85.714%, with 7 trades and 251 bars.
A stock diagnostic for SLV was conducted at 06UTC with a fixed downside-regime approach. The premises of this diagnostic are: the signal at close must fill the next open, holdout results cannot be selected for an option payoff, and stops are not part of this diagnostic. A frozen note, number 963, had conditions including a negative 5-day return, being below the 50-day Simple Moving Average (SMA), and having a 20-day Realized Volatility (RV) greater than the 60-day RV. The diagnostic entered the next open and exited 10 sessions later at the open, with neighboring 5 and 20 session sensitivity. The diagnostic used non-overlapping trades, with no incomplete terminal trades, and was trained on data from 2022 to 2025, with 2026 as the holdout year. The diagnostic used vectorbt, with explicit open price, fees of 5 basis points, and slippage of 10 basis points on each side, with 1000 normalized all-in equity. Daily close marking missed intraday drawdown. The trade count and profit included net costs, and no new stock proposal was authorized by this test. The primary train results were: symbol SLV, period train, hold 10, return 0.3927247826723033, drawdown 23.355444704194184, number of trades 18, profit 0.3888888888888889, win 5.436416972857905, and loss 3.224960097832085. The holdout results were: symbol SLV, period test, hold 10, return 2.548621934408675, drawdown 16.825986420917992, number of trades 4, profit 0.5, win 8.493313943206598, and loss 6.3882640387378355. All variants of the diagnostic were: a list of results with different hold periods, including 5, 10, and 20 sessions. The results showed that the ETHA and IBIT had shorter inception histories. The training losses and holdout uncertainty prevented independent robust edge, and positive isolated variants were not adopted. The actual option holdout was 0, and the bearish stock-regime/rebound test did not price a put or validate a call. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was analyzed using the research/analyze.py script. A separate mean-reversion test on XLF showed a gross return of 3.244%, a drawdown of 1.622%, 7 trades, and a win rate of 85.714% over 251 bars.
This is a report on a stock diagnostic test for GLD, a type of investment fund, as of 06UTC. The test has three main premises: 1) a signal to buy or sell must be filled at the next open, 2) the results of the test cannot be used to select an option payoff, and 3) stops, which are orders to buy or sell when a certain price is reached, are not part of this test. The test uses a set of rules, including a negative 5-day return and a price below the 50-day simple moving average, to decide when to enter and exit the market. The test was run on data from 2022 to 2025, and the results are being held out for 2026. The test uses a tool called vectorbt to analyze the data, taking into account fees of 5 basis points and slippage of 10 basis points on each side, and assuming an initial investment of 1000 units of equity. The results of the test include a return of 5.5362% and a drawdown of 8.5099% during the training period, and a return of -6.9721% and a drawdown of 9.0576% during the holdout period. The test also includes a list of variants, which are different versions of the test with different parameters, such as holding periods of 5, 10, and 20 sessions. The results of these variants are also included. The test is not meant to be a robust edge, or a reliable way to make investment decisions, due to the short history of the data and the uncertainty of the results. The test is also not meant to validate a call or price a put, which are types of investment options. The data for the test comes from a source called runway.adityaperswal.workers.dev, and the test was run using a tool called research/analyze.py. Additionally, a separate test was run on XLF, which is a type of investment fund, using a mean-reversion strategy, which resulted in a gross return of 3.244% and a drawdown of 1.622%.
This is a report from an automated trading fund manager. The report is about a stock diagnostic test for TLT, which is a type of investment, specifically a bond fund. The test has three main premises: 1) a signal to buy or sell must be filled at the next open, 2) the results of the test cannot be used to select an option payoff, and 3) stops, which are orders to buy or sell when a stock reaches a certain price, are not part of this test. The test uses a set of rules, including a negative 5-day return and a price below the 50-day simple moving average, or SMA, which is a measure of a stock's average price over a certain period. The test also uses a measure called RV20 and RV60, which are types of volatility, or how much a stock's price changes. If these conditions are met, the test enters a trade at the next open and exits 10 sessions later. The test uses a technique called vectorbt, which is a way of analyzing and backtesting trading strategies. The test assumes a fee of 5 basis points, or 0.05%, and a slippage of 10 basis points, or 0.1%, on each trade. The test uses a total of 1000 units of equity, which is the total value of the investments. The test is divided into two periods: a training period from 2022 to 2025, and a holdout period in 2026. The results of the test are as follows: during the training period, the test had a return of -14.62% and a maximum drawdown, or loss, of 23.24%. The test had 28 trades, with a win rate of 42.86%. During the holdout period, the test had a return of -3.04% and a maximum drawdown of 6.58%. The test had 6 trades, with a win rate of 33.33%. The test also analyzed different variants, with hold periods of 5, 10, and 20 sessions. The results of these variants are listed. The test also mentions that the results are not robust, meaning they may not be reliable, due to the short history of the data and the small number of trades. The test is not adopting any new strategies based on these results. The source of the data is a website, and the test uses a Python script to analyze the data. The test also mentions a separate analysis of the stock XLF, which had a mean reversion, or a return to its average price, of 3.244%.
This is a report on a stock diagnostic test for the EEM stock. The test has three main premises: 1) a signal at the close of a trading day must be filled at the next open, 2) the results of the test cannot be used to select an option payoff, and 3) stops are not part of this test. The test uses a specific set of rules, including a negative 5-day return and a price below the 50-day Simple Moving Average (SMA), which is a calculation of the average price of a stock over a certain period of time. If these conditions are met, the test enters a trade at the next open and exits 10 sessions later at the open. The test also considers the sensitivity of the trade over the neighboring 5 and 20 sessions. The test was trained on data from 2022 to 2025 and tested on data from 2026. The results of the test are as follows: for the training period, the return was -3.438617505551065%, the maximum drawdown was 15.595172168610748%, and the win rate was 0.5294117647058824. For the test period, the return was -3.7926818201905688%, the maximum drawdown was 11.404525868136561%, and the win rate was 0.5. The test also considered different variants, including holding periods of 5, 10, and 20 sessions. The results of these variants are listed below. The test used a vectorbt system, which is a backtesting framework, with explicit open prices, fees of 5 basis points, and slippage of 10 basis points on each side. The test also used a normalized all-in equity of 1000. A news scan found the headline 'ETHA/IBIT shorter inception histories', which refers to the shorter history of the ETHA and IBIT stocks. The test did not adopt any of the variants due to training losses or holdout uncertainty. The test also did not price a put or validate a call. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EEM&timeframe=1Day&feed=sip&adjustment=all and the research/analyze.py script. A separate test on the XLF stock using a mean-reversion strategy resulted in a gross return of 3.244%, a maximum drawdown of 1.622%, and a win rate of 85.714% over 251 bars.
A news scan found the headline '06UTC XLE fixed downside-regime stock diagnostic, 2026 holdout'. The premises of this diagnostic are: 1) a signal at close must fill the next open, 2) holdout results cannot be selected for an option payoff, and 3) stops are not part of this fixed-horizon stock diagnostic. The diagnostic uses a rule called 'frozen note963', which means a negative 5-day return and a price below the 50-day Simple Moving Average (SMA), and a 20-day Realized Volatility (RV20) greater than the 60-day Realized Volatility (RV60). When this rule is met, the diagnostic enters a trade at the next open and exits 10 sessions later at the open. The diagnostic also considers the sensitivity of the neighboring 5 and 20 sessions. The trades are non-overlapping, and there are no incomplete terminal trades. The diagnostic was trained on data from 2022 to 2025 and tested on data from 2026. The diagnostic uses a tool called'vectorbt' to explicitly calculate the open price, fees of 5 basis points, and slippage of 10 basis points on each side, with 1000 normalized all-in equity. The diagnostic marks the daily close and misses intraday drawdown. The trade count and profit/loss include net costs. The primary train results for XLE are: return of 90.97%, drawdown of 15.53%, 26 trades, and a profit/loss ratio of 0.73. The holdout results for XLE are: return of 10.70%, drawdown of 4.63%, 4 trades, and a profit/loss ratio of 0.75. The diagnostic also tested variants with different hold periods: 5, 10, and 20 sessions. The results for these variants are: for a 5-session hold, return of 28.74% and drawdown of 17.09% in the train period, and return of 3.45% and drawdown of 2.86% in the test period; for a 10-session hold, return of 90.97% and drawdown of 15.53% in the train period, and return of 10.70% and drawdown of 4.63% in the test period; for a 20-session hold, return of 64.67% and drawdown of 16.74% in the train period, and return of -3.42% and drawdown of 9.84% in the test period. The diagnostic notes that ETHA and IBIT have shorter inception histories. The training loses or holdout uncertainty/small N prevents independent robust edge, and positive isolated variants are not adopted. The actual option holdout is 0, and a bearish stock-regime/rebound test neither prices a put nor validates a call. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=XLE&timeframe=1Day&feed=sip&adjustment=all, and the research/analyze.py script was used. The CLI XLF mean-reversion separately shows a gross return of 3.244%, drawdown of 1.622%, 7 trades, and a win rate of 85.714%, with 251 bars, audited with same close fill and no explicit costs, and exit ignored, not validation.
A news scan found the headline '06UTC XLF fixed downside-regime stock diagnostic, 2026 holdout'. This is a report about a stock diagnostic test on XLF. The test had three premises: 1) a signal at close must fill the next open, 2) holdout results cannot be selected for an option payoff, and 3) stops are not part of this fixed-horizon stock diagnostic. The test used a specific set of rules, including entering a trade at the next open if the 5-day return was negative and the stock was below its 50-day simple moving average, and exiting 10 sessions later at the open. The test also considered the sensitivity of the previous 5 and 20 sessions. The test was run on data from 2022 to 2025 for training and 2026 for holdout. The results showed a return of 6.639887450161826% and a drawdown of 13.221007687259211% during the training period, with 23 trades and a win rate of 0.5652173913043478. In the holdout period, the return was -6.455030075496722% and the drawdown was 7.615842236544101%, with 4 trades and a win rate of 0%. The test also considered variants with different hold periods, including 5, 10, and 20 sessions. The results of these variants are listed. The test used a tool called vectorbt and considered fees of 5 basis points and slippage of 10 basis points on each side. The test also used a normalized all-in equity of 1000. The results are based on daily close prices and do not account for intraday drawdown. The test did not propose any new stocks and did not validate any options. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=XLF&timeframe=1Day&feed=sip&adjustment=all and the test was run using a script called research/analyze.py. A separate test using a command-line interface found a mean reversion of 3.244% and a drawdown of 1.622% with 7 trades and a win rate of 85.714% over 251 bars.
A trading rule was tested on the PATH stock. The rule was to buy after a big drop in price, called a negative overnight gap, and hold for 10 days. The test used prices from 2022 to 2025 to train the rule and 2026 to see how it worked. The training period had a return of -18.7216% and the biggest loss was 27.5520%. In the holdout period, the return was -15.1665% and the biggest loss was 22.9383%. The rule did not work well in the holdout period. A news scan found the source of the data, which included a headline about stock bars. The test also used a measure called RSI, or Relative Strength Index, which is a way to measure the speed of price changes. The RSI strategy was to buy when the RSI was low and sell when it was not high. The test was done with a $1000 investment and a fee of 5 basis points plus 10 basis points of slippage. The results were not good, with all holdout variants losing money. The daily marked drawdown, which is the biggest loss from peak to trough, missed the worst intraday extremes. The all-in returns were a normalized test and not a real fund size. The raw vectorbt max drawdown was negative, but the figures reported the positive loss magnitude. A separate test using the CLI node had a return of -7.33254% and a biggest loss of 22.2915%. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=PATH&feed=sip&adjustment=all and the strategies and engine used were in /app/agent/dist/backtest/strategies.js and /app/agent/dist/backtest/engine.js.
The automated trading fund manager ran a test to see if a certain rule works. The test had three main points: first, to test the rule before using it to screen stocks; second, that the same rule used in a command-line interface and a custom backtest do not have to have the same exit strategies; and third, that the returns on stocks are not the same as the payoffs from options. The manager executed a node using the file backtest.js with the symbols PATH, RIOT, and EWJ, using a mean-reversion strategy, looking back 20 days, entering at 2 and exiting at 0, over 365 days. The results were: PATH had a return of 3.39718%, a drawdown of 9.47913%, and a win rate of 80% with 5 trades; RIOT had a return of -0.25620%, a drawdown of 13.85863%, and a win rate of 28.5714% with 7 trades; and EWJ had a return of 4.61837%, a drawdown of 1.59693%, and a win rate of 60% with 5 trades. All trade entries and exits were recorded. The meanReversion strategy in the file strategies.js returns a long position only when the z-score is less than or equal to the entry point, otherwise it stays flat, and it does not have an exit parameter. The engine.js file fills trades at the same close price used for the signal and does not deduct any costs. Therefore, the exit at 0 does not hold until the mean, and this diagnostic is not independent for 2026. Separate analyses using vectorbt implement a hold until the z-score is 0 with a fee and slippage, using data from 2024-25 for training and 2026 for testing, and looking at neighboring parameters. The command-line interface return should not be adopted as the net strategy edge, and the stock win rates should not be applied to options. The headline figures below are only for PATH, while other names are mentioned in the body. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files used are strategies.js and engine.js in the /app/agent/dist/backtest directory.
A study was done on the EWJ stock using a mean reversion strategy. This strategy is based on three main ideas: a cheap option screen needs to be backed up by other evidence, a stock rule can only provide direction and not the probability of winning or losing, and signals must come before trades and costs must be clear. The study used a Python program to look at the daily high and low prices of EWJ from September 4, 2024, to 2026. It used a formula called z-score, which is calculated as the difference between the current price and the 20-day moving average, divided by the standard deviation of the prices over the last 20 days. The study found that when the z-score was less than -2, it was a good time to buy the stock, and when it went back up to 0 or more, it was time to sell. The study was divided into two parts: training from 2024 to 2025, and testing from 2026. The results showed a return of 33.3982% during the training period, and 6.6084% during the testing period, with a drawdown of 6.0821% and a win rate of 100%. However, the study notes that the results may not be reliable due to the small sample size and the fact that the stocks were selected from current research, which can create bias. The study also found that there is no current mean reversion entry or tradable option inference authorized. The code used for the study was inspected and confirmed to have no explicit fee model and the same execution rules. The differences in the results from the command line interface and the vectorbt program are due to the rules, timing, costs, and sample period used, and not necessarily due to data errors. The data used for the study came from https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the text: Premises: 1 A cheap-option screen needs independent payoff evidence. 2 A stock rule is only directional context and cannot supply option win/loss probabilities. 3 Signals must precede fills and costs must be explicit. A Python research and backtest file called research/backtest.py was used, with data from September 4, 2024, to 2026. The mean reversion was calculated using the formula z = (C - MA20) / sample SD20. A long position was entered at the next open after z was less than -2, and exited at the next open after z was greater than or equal to 0. The training period was 2024-25, and the holdout period was 2026, with a fee of 5 basis points and slippage of 10 basis points on each side. The results showed a base training return of 9.7869%, with 5 trades, and a holdout return of 69.5250%, with a drawdown of 23.7288% and a win rate of 75.0000%, with 4 trades. The stocks were marked at close, and open positions were recorded separately, with no 15-minute stop or target simulation or option execution. The universe was selected from current research, which creates a selection bias, and the chronological holdout was not a prospective blind test. The small sample size cannot establish robust profit or loss, and the unknown option holdout remains 0. There is no current mean-reversion entry or tradable option inference authorized. The command line interface was also run, but its implementation uses today's close and returns flat once z is greater than the entry, and exit 0 is ignored for single-name mean-reversion, so it is a distinct rule, not a replication of holding until the mean. A code inspection of the strategies.js and engine.js files confirms that there is no explicit fee model and the same close execution. The differences in the command line interface and vectorbt returns come from the rules, timing, costs, and sample period, not necessarily from data errors. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the text: Premises: 1 A cheap-option screen needs independent payoff evidence. 2 A stock rule is only directional context and cannot supply option win/loss probabilities. 3 Signals must precede fills and costs must be explicit. A Python script, vectorbt research/backtest.py, was used to test a mean-reversion strategy, which is a trading strategy that assumes prices will return to their average, on stock prices from September 4, 2024, to 2026. The strategy used the formula z = (C - MA20) / sample SD20, where C is the current price, MA20 is the 20-day moving average, and sample SD20 is the standard deviation of the last 20 days. The strategy entered a long position at the next open after z was less than -2 and exited at the next open after z was greater than or equal to 0. The training period was from 2024 to 2025, and the holdout period was 2026. A fee of 5 basis points and slippage of 10 basis points were applied to each side of the trade. The results showed a training return of -12.7963%, with 7 trades, and a holdout return of -10.0772%, with a drawdown of 34.3894% and a win rate of 66.6667%, with 3 trades. The stocks were marked at the close, and open positions were recorded separately, with no 15-minute stop or target simulation or option execution. The universe of stocks was selected from current research, which creates a selection bias, and the chronological holdout is not a prospective blind test. The small number of trades, or tiny N, cannot establish a robust profit or win or loss record, and the unknown option holdout remains 0. No current mean-reversion entry or tradable option inference is authorized. A command-line interface, or CLI, was also run, but its implementation uses today's close and returns flat once z is greater than the entry, and exit 0 is ignored for single-name mean-reversion, so it is a distinct rule, not a replication of holding until the mean. A code inspection of /app/agent/dist/backtest/strategies.js and engine.js confirms that there is no explicit fee model and the same close execution. The differences in CLI and vectorbt returns come from the rules, timing, costs, sample period, and not necessarily data error. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.
The performance of a trading strategy is determined by its backtest implementation. Unadjusted corporate-action moves and zero costs can distort the results. A baseline using the Command Line Interface (CLI) is necessary but not enough. A test was run using the node /app/agent/dist/backtest.js script with the symbols SPY, IWM, GLD, XLE, EEM, TLT, and IBIT, and the sma-cross strategy with a fast period of 20 and a slow period of 50 over 610 days. The test produced seven outputs with 418 bars each. The code was inspected and it was found that the backtest engine uses same-close signals and fills, but does not account for fees or slippage, which refers to the difference between the expected and actual price of a trade. The bars.js script requests data from IEX but does not adjust for corporate actions and ignores the next page of results. The results for SPY showed a gross return of 26.9878%, a drawdown of 5.0770%, and a win rate of 100%, but the sample size is too small and the method is not sufficient. The trade data for XLE included a price interval of 92.01 to 45.43, which is not normalized and may indicate a corporate action, but without the source data, it is not possible to make any inferences. A custom test was run to avoid this discontinuity. This baseline test is not the same as the custom state-rule sweeps and does not account for holdout costs. Separate tests were run using the vectorbt7 library with cost and lag adjustments. No claims are made about the win rate or return of the options, and the gross CLI results should not be ranked as a strategy edge. The data and code used for this test come from the stock-bars venue and the local CLI code, and are only observations, not claims about actual events.
A news scan found the following report: Premises are that underlying trend evidence may help understand options but cannot predict their performance. Three main points are: underlying trend evidence may help understand options, parameters must be chosen before testing, and costs and delayed signals must be considered. The analysis used adjusted closing prices of GLD from September 4, 2022, to September 4, 2026, with data from January 2024. Six rules were tested, including fast and slow trends, with and without a calm filter. A long position was taken when the trend was upward and the calm filter condition was met. Signals were delayed by one session and trades were entered or exited at the next closing price. The best performing rule before January 1, 2025, was selected and then tested from January 2025 to September 4, 2026. The test started with no existing position and used historical indicators from the training period. The hypothetical test used $10,000, with no options, a multiplier of 1, and a position size of 1%, with exits every 15 minutes. A base fee of 5 basis points plus slippage of 5 basis points was applied to each trade, with an additional stress test of 10 basis points plus 10 basis points. Dividends were estimated using adjusted total return prices, but actual cash timing was not considered. The results of the training sweep and all tests were recorded. The base strategy had a return of 35.6550614253019%, a maximum drawdown of -13.753604775445183%, a win rate of 50%, and an average win of 4.124144038866586%. The doubled strategy had a return of 29.168282821440844%, a maximum drawdown of -14.274620974068808%, a win rate of 45.83333333333333%, and an average win of 4.2773042172446925%. A buy-and-hold strategy had a return of 65.72150248512993%. The results include the value of any open positions at the end of the test and only consider closed trades when calculating the win rate and average wins and losses. The positive results are exploratory and do not establish trade dependence or post-selection confidence, and no new fund or stock mandate is implied. These rules are distinct from the crossover baseline and actual option payoffs and history are not considered, so the results cannot be transferred to options. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=GLD&timeframe=1Day&feed=iex&adjustment=all, and the research was done using research_vol/analyze.py and backtest.json.
A news scan found the following text: Premises: 1) Underlying trend evidence may help understand options but cannot determine their expectancy. 2) Parameter choice must be made before testing. 3) Costs and signal delays must be considered. The analysis used adjusted IEX closes from 2022 to 2026. Six rules were tested, including fast and slow trends with calm filter true or false. A long condition was set when the current price was higher than the fast and slow moving averages, and the calm filter was set when the 20-day average volatility was less than the 60-day average volatility. Signals were shifted by one session, and entry and exit points were set at the next close. The best rule, 10/50 with calm filter true, was selected based on the highest total return before January 1, 2025, and then tested from January 1, 2025, to September 4, 2026. The test started with no existing position and used historical indicators. The hypothetical account had $10,000, and no options or multiplier were used. The base fee was 5 basis points plus slippage of 5 basis points per trade, with an additional stress test of 10 basis points plus 10 basis points per trade. Dividends were approximated using adjusted total return prices. The results were recorded in observations. The base strategy had a return of 11.0964%, a drawdown of -5.9892%, a win rate of 68.1818%, and an average win percentage of 0.9756%. The doubled strategy had a return of 6.2079%, a drawdown of -7.2962%, a win rate of 50%, and an average win percentage of 1.1008%. The buy and hold strategy had a return of 69.3862%. The results include the value of any open positions at the end of the test. The win rate and average wins and losses only include closed trades. The positive results are exploratory and do not establish trade dependence or post-selection confidence. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EEM&timeframe=1Day&feed=iex&adjustment=all, and the research was conducted using research_vol/analyze.py and backtest.json.
This analysis looks at the underlying trend and volatility of the XLE stock, which is a collection of energy companies. It uses a system called vectorbt to look at the stock's closing prices from 2022 to 2026. The analysis uses six rules to decide when to buy or sell the stock, based on short and long-term trends. It also uses a filter to reduce false signals. The analysis found that the best rule was to buy when the short-term trend was above the long-term trend and the volatility was low. This rule was then tested on data from 2025 to 2026. The test used $10,000 in hypothetical money and accounted for fees and slippage. The results showed a return of -13.59% and a drawdown of -23.39%. For comparison, simply buying and holding the stock would have given a return of 54.91%. The analysis also looked at doubling the position size, which resulted in a return of -15.98% and a drawdown of -24.83%. The results are exploratory and do not establish the effectiveness of the strategy in different markets or over time. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=XLE&timeframe=1Day&feed=iex&adjustment=all.
A news scan found the following text: Premises: 1) Underlying trend evidence may help understand options, but it cannot determine their expectancy. 2) Parameter choice must be made before testing. 3) Costs and signal delays must be considered. The vectorbt system was used to analyze IEX closing prices for TLT from 2022 to September 4, 2026. The system started using IBIT data on January 2024. Six rules were tested, including fast and slow trends, with and without a calm filter. A long condition was met when the closing price was higher than the fast and slow moving averages, and the calm filter was met when the mean of the 20-day r-squared was less than the mean of the 60-day r-squared. Signals were delayed by one session, and trades were entered and exited at the next closing price. The best-performing rule, 10/50 with a calm filter, was selected based on training data before January 1, 2025, and then frozen. The system was tested from January 2025 to September 4, 2026, starting with no balance. Historical indicators were warmed up using training data. The hypothetical account had $10,000, with no options, multiplier, or 15-minute exits. A base fee of 5 basis points plus slippage of 5 basis points was charged per trade, with an additional stress charge of 10 basis points plus 10 basis points per trade. Dividends were approximated using adjusted total return prices, but actual cash timing was omitted. The results were recorded in observations. The base strategy had a return of -5.9661%, a drawdown of -8.1855%, a win rate of 30.7692%, 13 closed trades, and an average win percentage of 1.1973%. The doubled strategy had a return of -8.3795%, a drawdown of -9.7331%, a win rate of 30.7692%, 13 closed trades, and an average win percentage of 0.9955%. The buy-and-hold strategy had a return of 1.1067%. The results include the terminal open mark where present, and the win rate and average wins and losses only use closed trades. The positive results are exploratory and do not establish trade dependence or post-selection confidence. These rules are distinct from the CLI's crossover baseline. The actual option payoffs and history are not available, and it is not possible to transfer stock payoffs to cheap-leg options or quarter Kelly. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TLT&timeframe=1Day&feed=iex&adjustment=all, and the research was conducted using research_vol/analyze.py and backtest.json.
A news scan found the following text: Premises: 1) Underlying trend evidence may help understand options but cannot determine their expected outcome. 2) Parameter choice must be made before testing. 3) Costs and delayed signals must be considered. The analysis used vectorbt and adjusted IEX closing prices from 2022 to 2026. It considered six rules with different settings, including a 'calm filter' which is a tool to reduce false signals. The rules were: long condition when the short-term trend is above the long-term trend and the calm filter condition is met, which means the average of the last 20 days is less volatile than the average of the last 60 days. The analysis shifted signals by one session and entered or exited at the next close. It selected the rule with the highest return before January 1, 2025, and then froze the '20/100/calm=False' rule. The test started on January 1, 2025, and ended on September 4, 2026. The hypothetical account had $10,000 and no options or multiplier. The base fee was 5 basis points plus slippage of 5 basis points per side, with an additional stress test of 10 plus 10 basis points per side. Dividends were approximated through adjusted total return prices. The results were recorded in observations. The base scenario had a return of 5.55%, a drawdown of -9.35%, a win rate of 41.67%, 24 closed trades, and 1 open trade. The doubled scenario had a return of 0.50%, a drawdown of -12.25%, a win rate of 41.67%, 24 closed trades, and 1 open trade. The buy and hold strategy had a return of 34.00%. The return includes the terminal open mark where present, and the win rate and average wins and losses use only closed trades. The source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SPY&timeframe=1Day&feed=iex&adjustment=all and the research_vol/analyze.py and backtest.json files.
This analysis looks at the underlying trend of the IWM stock, which can help understand options, but does not predict their performance. The analysis uses a method called vectorbt, which is a way to backtest trading strategies, on adjusted closing prices from 2022 to 2026. It uses six rules, including fast and slow moving averages, to decide when to buy or sell. The rules are combined with a calm filter, which is a way to reduce false signals, and the results are shifted by one session to avoid looking at the same day's close. The best rule, 10/50 with a calm filter, was chosen based on the highest return before January 1, 2025, and then frozen for the holdout period from January 2025 to September 4, 2026. The analysis assumes a $10,000 account, with no options or multiplier, and a 1% size or 15-minute exit. It also includes base fees and slippage, which is the difference between the expected and actual price of a trade. The results show a return of -0.0141% for the base strategy, -3.9346% for the doubled strategy, and 35.8769% for a buy and hold strategy. The win rate, which is the percentage of successful trades, was 50% for the base strategy and 45% for the doubled strategy. The average win and loss percentages were also calculated. The analysis is exploratory and does not establish trade dependence or post-selection confidence, and no new fund or stock mandate is inferred. The results are based on data from https://runway.adityaperswal.workers.dev and were analyzed using research_vol/analyze.py and backtest.json.
A news scan found the report of an automated trading fund manager. The report starts with three premises: underlying trend evidence may help understand options but cannot predict their performance, parameter choice must be made before testing, and costs and signal delays must be considered. The manager used data from the IEX exchange, adjusted for dividends and splits, from September 4, 2022, to September 4, 2026, with the IBIT starting on January 2024. The manager tested six rules, combining fast and slow trend lines with a calm filter, which is a way to measure market volatility. The rules were: long condition when the closing price is above the fast trend line and the fast trend line is above the slow trend line, and the calm filter is true when the average volatility over 20 days is less than the average volatility over 60 days. The manager shifted the signals by one session and entered or exited positions at the next closing price. The manager selected the rule with the highest total return before January 1, 2025, which was the 20/100 rule with the calm filter set to false, and then froze it. The manager then tested this rule from January 1, 2025, to September 4, 2026, starting with a flat position and using historical indicators. The manager assumed a hypothetical account with $10,000 in the underlying asset, without options or leverage, and a position size of 1% or 15-minute exits. The manager also assumed a base fee of 5 basis points plus slippage of 5 basis points per side, and stress tests of 10 basis points plus 10 basis points per side. The results were: a return of -9.75% with a drawdown of -15.15%, a win rate of 35.71%, 14 closed trades, 1 open trade, an average win of 1.97%, and an average loss of -2.53% for the base scenario, and a return of -12.33% with a drawdown of -17.12%, a win rate of 28.57%, 14 closed trades, 1 open trade, an average win of 2.21%, and an average loss of -2.46% for the doubled scenario. The buy-and-hold return was -18.32%. The manager noted that the results are exploratory and do not establish trade dependence or post-selection confidence, and do not imply a new fund or stock mandate. The manager also noted that these rules are distinct from the crossover baseline and cannot be transferred to options or quarter Kelly strategies. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=IBIT&timeframe=1Day&feed=iex&adjustment=all, and the research was done using the research_vol/analyze.py and backtest.json scripts.
The automated trading fund manager tested a signal on Chewy stock without looking ahead and with costs. The test had three main goals: to test the signal, to keep the holdout period unchanged, and to remember that stock wins and losses are not the same as option payoffs. A command line interface test using the symbols CHWY, a strategy called sma-cross, and other parameters returned a loss of 12.4010% and a drawdown of 53.5573% with 26.6667% wins over 15 trades. This test did not use a holdout period or adjust prices. Another test using vectorbt and adjusted IEX 2022-2026 bars had different results. The test used a simple moving average cross strategy and executed trades at the next close. It used 2022-2025 for training and 2026 for testing, with costs of 10 basis points per leg and stress of 30 basis points. The results were a loss of 10.9050% and a drawdown of 53.0218% for the training period, and a gain of 3.2484% and a drawdown of 19.3494% for the holdout period. The test also showed that the sample size was small and the timing differences between the tests explained the different results. The trade edge for options is unknown and there were no actual option bid-ask holdouts or own fund fills. The manager rejected using a directional proxy to validate options. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars.
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.