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.
This is a description of a trading rule for the IWM stock symbol. The rule is based on two moving averages, a short-term one with a 10-day period, known as SMA10, and a long-term one with a 50-day period, known as SMA50. A moving average is a way to smooth out price data to see trends more clearly. The rule says to buy when the short-term average is higher than the long-term average and sell when it is not. The rule was tested on data from 2022 to 2025 and then applied to data from 2026 up to September 4. The test results showed a return of 7.763% with a maximum loss of 17.333% during the training period, and a return of 11.401% with a maximum loss of 7.001% during the test period. The data used for the test came from the IEX feed and included 1173 daily bars for the IWM symbol. The test was run using a program called vectorbt and the results were checked using a command-line interface. The test had some limitations, including a small number of trades and only using data from one source. Because of these limitations, it is hard to draw conclusions about how well the rule will work in the future. Additionally, there was no data on option returns, so no options were bought or sold.
A trading fund manager made some notes about the GLD stock. The manager had three main ideas: signals must happen before trades are made, costs and time periods must be clear, and making money from stocks does not mean making money from options. The manager made a rule: if the average price of GLD over 10 days is higher than the average price over 50 days, then buy the stock and sell it the next day. 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 also considered fees and slippage of 5 basis points per trade and stress of 15 basis points. The manager tested the strategy on GLD from 2022 to 2025 and got a return of 77.71% with a maximum loss of 12.13%. The manager then tested the strategy from 2026 to September 4 and got a return of 1.52% with a maximum loss of 20.31%. The manager used 1173 daily bars of data from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The manager also used a tool called vectorbt and a command line interface to run the strategy on other stocks. However, the manager notes that the sample size is small, the selection of stocks is limited, and the market conditions are changing, so it is hard to make conclusions. The manager also notes that not losing money in a small sample does not mean that there is no risk of losing money in the future.
A trading strategy for Netflix stock, ticker NFLX, was tested. The strategy is called mean-reversion, which means it tries to find when the stock price will return to its average. Premises for the test were: the strategy must be implemented exactly as defined, adjusted prices must be used, and the test must be run on a holdout window, which is a set of data not used to train the strategy. The required command line interface, or CLI, command was: node /app/agent/dist/backtest.js --symbols NFLX --strategy mean-reversion --lookback 20 --entry 2 --exit 0 --days 1344. The CLI returned a result of +25.683% profit, with a maximum drawdown of 11.605%, and a win rate of 61.538%. However, the source code audit found that the adjustment was omitted, and the strategy was not implemented correctly. A different test using vectorbt on 922 adjusted IEX bars was run, with a mean-reversion strategy that buys when the z-score is less than -2 and exits when the z-score is greater than or equal to 0. The test was run with a fee and slippage of 5 basis points each, and a stress test of 30 basis points. The results were: +50.793% profit on the training set, and -6.395% profit on the test set. A sweep of threshold values from 1.5 to 2.5 was run, and all results were negative. The underlying long mean-reversion is a diagnostic, not a trading strategy, and there is no inference for option probability of win, as no actual option bid-ask history was given prior to OPRA 403. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=NFLX&timeframe=1Day&start=2023-01-01&end=2026-09-05&adjustment=all&feed=iex.
A trading strategy for EWZ, a type of investment fund, has a problem. The strategy is based on an idea called mean-reversion, which means it tries to buy when prices are low and sell when prices are high. The strategy uses a measure called z-score, which is a way to compare how different a price is from the average price. The strategy buys when the z-score is less than or equal to -2 and sells when it is greater than or equal to 0. There is a delay of one day before buying or selling again. The strategy also takes into account fees and slippage, which are costs of buying and selling. The strategy was tested on data from January 2023 to December 2025 and then applied to data from January 2026 to September 4. The results were not good, with a return of 0.3071% and a big loss of 11.3997%. When the costs were increased to 30 basis points, the return became negative. A separate test was run using a command-line interface, which is a way to give instructions to a computer using text. The test used different investments, including SNAP, ETHA, and EWZ, and got different results. The test showed that EWZ had a positive return of 13.40%, but this result is not reliable because the test was not done independently. The strategy does not work well with options, which are a type of investment that gives the buyer the right to buy or sell something at a certain price. The data used for the test came from a website and included stock prices from January 1, 2023, to September 5, 2026.
A news scan found the report that the ETHA fixed mean-reversion holdout cannot validate long-option purchases. The premises of the strategy are that underlying returns are used for diagnosis, not option bid or ask returns, the signal must execute after it is known, and strategy parameters cannot be selected on holdout. The strategy uses a fixed 20-day population z-score, entering long when z is less than or equal to -2, and exiting when z is greater than or equal to 0, with a one-bar delay to the next close. The strategy also includes fees of 5 basis points and slippage of 5 basis points on each side, totaling 10 basis points, and stress of 15 plus 15 basis points. The training period is from January 2023 to December 2025, with the inception of ETHA in 2024, and the holdout period is from January 2026 to September 4. The baseline holdout return is -27.9810%, the drawdown is 37.4002%, the win rate is 0%, the average win is undefined, and the average loss is 15.143407363395564%. At 30 basis points, the return is -28.5549%. A separate command-line interface run with the node /app/agent/dist/backtest.js script and the symbols SNAP, ETHA, and EWZ, using the mean-reversion strategy, lookback of 20, entry of 2, exit of 0, and days of 365, produced the following results: SNAP had a return of -14.86%, a drawdown of 23.97%, a win rate of 62.5%, and 8 trades, ETHA had a return of -18.19%, a drawdown of 32.04%, a win rate of 66.67%, and 6 trades, and EWZ had a return of 13.40%, a drawdown of 0.60%, a win rate of 90%, and 10 trades. Prior source code inspection shows that the CLI exit 0 is unused and the flat as entry threshold clears, hence it is a different rule and not an independent reproduction. Vectorbt explicitly implements the noted mean exit. The holdout is tiny, and there is no option history, even with a positive EWZ underlying cell, which does not establish a cheap call or put. There is no option quarter Kelly edge, and the size is 0. The source venue stock bars adjustment is all, feed IEX, from 2023-01-01 to 2026-09-05, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=ETHA&timeframe=1Day.
A news scan found the headline about a trading fund manager's notes. The manager's premises are that underlying returns are used to diagnose issues, not option bid or ask returns, and signals must be executed after they are known. The strategy parameters cannot be chosen based on the holdout data. The manager uses a fixed 20-day population z-score, entering a long position when the z-score is less than or equal to -2 and exiting when it is greater than or equal to 0, with a one-bar delay to the next close. The manager also considers fees of 5 basis points and slippage of 5 basis points on each side, totaling 10 basis points, and stress of 15 plus 15 basis points. The training period is from January 2023 to December 2025, and the holdout period is from January 2026 to September 4. The manager forces a flat position at the end of each sample and sweeps 1.5, 2, and 2.5 without choosing a winner. The baseline holdout return is -20.0435%, the drawdown is 27.6275%, the win rate is 20%, and the average win is 5.694649678738458%. At 30 basis points, the return is -21.6267%. A separate command-line interface run shows different results for SNAP, ETHA, and EWZ. Prior source code inspection shows that the command-line interface exit 0 is unused and the flat as entry threshold clears, hence it is a different rule and not an independent reproduction. The vectorbt explicitly implements the noted mean exit. The holdout is tiny and there is no option history, even for EWZ, which has a positive underlying cell but does not establish a cheap call or put. There is no option quarter Kelly edge, and the size is 0. The source venue stock bars adjustment is all, feed iex, from 2023-01-01 to 2026-09-05, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SNAP&timeframe=1Day.
A study was done to see if buying long options for IBIT, based on a mean-reversion strategy, would be profitable. The study looked at the stock's past performance, including its profitability and costs, to make a decision. A diagnostic test was run using a computer program called CLI, which looked at the stocks F, SOFI, and IBIT, using a mean-reversion strategy over 20 days, with specific entry and exit rules. The results showed that F had a return of 0.79%, SOFI had a return of 7.07%, and IBIT had a loss of 25.80%. However, these results were not used because they did not account for certain costs and execution times. Another test was done using a different method, which looked at the performance of the stocks over a longer period, from January 2023 to September 2026, with different thresholds and costs. The results showed that even with a 2-sigma threshold and a 10bp cost, the return was still negative, at -24.461%. The study concluded that there was not enough evidence to support buying long options for IBIT, due to small sample sizes, lack of historical bid/ask execution data, and insufficient profits in the underlying stock to cover the costs of the options. A strong counterexample was found, where the rebound was too small to recover the spread and theta. The data used for the study came from a source at https://runway.adityaperswal.workers.dev. Based on the results, it was decided not to use this strategy to size options for IBIT.
A study on SOFI stock used a mean-reversion strategy, which is a trading method that assumes prices will return to their average, to see if it would be profitable to buy long options. The study had two main points to consider: first, that making a profit on the stock itself is not enough to guarantee making a profit on options, and second, that the costs of buying and selling must be included in the calculation. The study used a computer program to test the strategy on SOFI, F, and IBIT stocks over a period of time. The results showed that SOFI had a 7.07% profit, but this was not enough to justify buying long options due to small sample sizes and lack of historical data on option prices. The study also found that the rebound in prices was too small to cover the costs of buying and selling options. Therefore, the study rejected using this strategy to decide how many options to buy.
A study on the stock F found that using a mean-reversion strategy, which is a trading method that assumes prices will return to their average, did not provide enough evidence to support buying long options. The study used a computer program to test the strategy on F and two other stocks, SOFI and IBIT, from January 2023 to September 2025. The results showed that F had a return of 0.79%, SOFI had a return of 7.07%, and IBIT had a return of -25.80%. However, these results were not reliable due to the small sample size and lack of data on option prices. Another test was done using a different method, which also did not find any evidence to support buying long options. In fact, the results showed that the potential profits from the strategy were not enough to cover the costs of buying and selling options. Therefore, the study concludes that using this strategy as a proxy to determine the size of options trades is not reliable.