A computer program checked the history of Bitcoin prices in US dollars over 1095 days. It used a special method called Simple Moving Average, or SMA for short, with two sets of rules: one that reacts quickly, called fast, set at 1, and one that reacts slowly, called slow, set at 50. The program looked at all the data, including the current day, without considering any fees or leaving out any information. However, the results are not reliable because some old data for a cryptocurrency called SOL is missing, and there are disagreements in the hourly and daily price records. The program also tested how costs and the timing of events affect the results, and it considered how the calendar affects the decision. Even though the overall performance was positive, it is not enough to justify investing. The data came from a website at https://runway.adityaperswal.workers.dev/data/crypto-bars.
This is a description of a trading strategy for the TLT fund. The strategy is based on three premises. First, it uses a protocol that freezes prices before they are used, and it looks for a decline in the Relative Strength Index, or RSI, which is a measure of how quickly a stock's price is changing, over three days, starting below 65, and the stock's closing price must be higher than its Simple Moving Average, or SMA, over 200 days. The strategy also looks for the RSI to be higher than 75 before entering or exiting a trade, with no overlap between trades. Second, the strategy uses a recent sample of data from 2016 to 2020 for training and from 2021 to 2025 for testing, with 2026 being the most recent period, and it does not use any parameters that are fitted to the data, but rather looks at the overall market period. Third, the strategy uses a cash long only approach, which means it only buys stocks and does not sell them short, and it uses a vectorbt 5 basis point fee plus 5 basis points for each trade, and it reconciles independent wealth, with daily close marked as DD, and adjusted prices that approximate cash distributions. The mean return is -0.4372989750850283% and the worst return is -2.205882735475273%. The conclusion is that this is only a baseline strategy and does not include stop or target or trail validation, and a profitable test period does not make up for earlier losses or justify entering a trade. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
The automated trading fund manager made the following premises: the protocol 596/598 was frozen before prices were considered, and the Relative Strength Index, or RSI, a measure of stock price movement, declined for 3 days starting below 65, with the close being greater than the Simple Moving Average, or SMA, of 200. The manager also considered a holdout chronological sample, where the years 2016-2020 were used for training, 2021-2025 were used for holdout, and 2026 was considered recent, with no parameters fitted, but the market period was previously inspected. The manager used an explicit cash long only vector with a 5 basis point fee and a 5 basis point slip each way, which reconciles independent wealth, with daily close marked and adjusted prices approximating cash distributions. The results showed a mean of -0.44454334934041495% and a worst of -5.272528748719929%. The conclusion is that this is only a baseline and does not validate stop, target, or trail, and a profitable holdout does not make up for a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs were retained.
A trading strategy for TLT has been set up with certain rules. First, the strategy looks at the Relative Strength Index, or RSI, which measures how fast prices are changing, and the Simple Moving Average, or SMA, which is the average price over a certain time. The rules are: the RSI must be below 65 and the price must be higher than the SMA200, and then the strategy will buy or sell at the next open. The strategy will also sell when the RSI is higher than 75. The strategy was tested on data from 2016 to 2020, and the results from 2021 to 2025 were used to check the strategy. The strategy only buys and holds, and it uses a 5 basis point fee and a 5 basis point slip, which is a small cost for buying or selling. The daily close is marked and the prices are adjusted to account for cash distributions. The results show a mean return of 0.2645556325254861% and a worst loss of -2.30449711384908%. This is just a baseline test and does not include stop or target prices. The strategy was profitable in the test period, but this does not make up for losses in earlier periods. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No changes were made to the strategy to make it more profitable, and all five ETFs were kept.
The automated trading fund manager made notes about the EEM investment. The manager started with three premises. First, the manager used a protocol with rules 596 and 598, which were set before prices were considered, and looked at a measure called RSI2, which had declined for three days starting from below 65, and the close was higher than the SMA200, a moving average of 200 days. The manager then decided to enter or exit the investment at the next open, after the RSI went above 75, without any overlap. Second, the manager looked at recent samples from 2016 to 2020 for training and from 2021 to 2025 as a holdout, and from 2026 as a recent sample, without adjusting any parameters, but had previously inspected the market period. Third, the manager used a long-only strategy with a 5 basis point fee and a 5 basis point slip in each direction, which was reconciled with independent wealth, and marked daily closes as DD, with adjusted prices that approximated cash distributions. The results showed a mean of 1.7633368092934865% and a worst result of -0.9457374891720383%. The manager concluded that this was only a baseline and did not validate stop, target, or trail strategies, and that a profitable holdout period did not make up for earlier negative periods or justify entering the investment. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
This report is based on certain premises. The first premise is that the Protocol 596/598 is frozen before prices are considered, and the Relative Strength Index (a measure of stock price movement) declines for 3 days starting below 65, with the close being greater than the Simple Moving Average (a trend line) of 200. The entry and exit points are determined by the next open after the Relative Strength Index is greater than 75, with no overlap. The second premise is that there is a holdout chronological sample, where the data from 2016 to 2020 is used for training, 2021 to 2025 is used for holdout, and 2026 is considered recent. No parameters are fitted, but the market period has been previously inspected. The third premise is that there is an explicit cash long only vector with a 5 basis point fee and a 5 basis point slip each way, which reconciles independent wealth. The daily close is marked and adjusted prices approximate cash distributions. The results show a mean of 0.01995432325801159% and a worst-case scenario of -6.621975820217618%. The conclusion is that this is only a baseline and does not validate stop, target, or trail. A profitable holdout does not make up for a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs were retained.
A news scan found the headline about a trading strategy. Premises for this strategy are as follows: the protocol, which is a set of rules, was frozen before prices were considered, and it uses a measure called RSI2, or Relative Strength Index, which declines for 3 days starting below 65, and the close is greater than the SMA200, which is a moving average. The strategy also uses an open entry and exit after the RSI is greater than 75, with no overlap. The training sample is in chronological order, with the years 2016-2020 used for training and 2021-2025 used for holdout, and 2026 is recent. There are no fitted parameters, but the market period was previously inspected. The strategy uses a cash long only vector, which means it only buys, with a 5 basis point fee and a 5 basis point slip each way, and it reconciles independent wealth. Daily close is marked as DD, and adjusted prices approximate cash distributions. The results show a mean of 0.10842871778365562% and a worst of -5.963594231989811%. The conclusion is that this is only a baseline, and it does not validate stop, target, or trail. A profitable holdout does not repair a negative earlier period or justify entry. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars, and the original source is https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs, including EEM, were retained.
The automated trading fund manager made the following premises: the Protocol 596/598 was frozen before prices were considered, and the Relative Strength Index (a measure of stock price movement) 2 declines for 3 days starting below 65, with the close being greater than the Simple Moving Average (a stock price average) 200. The manager also considered the recent chronological sample, which includes data from 2016 to 2020 for training and 2021 to 2025 for holdout, and 2026 for recent data, with no parameters fitted, but the market period was previously inspected. The manager used an explicit cash long only vector with a 5 basis point fee and 5 basis point slip each way, which reconciles independent wealth, with daily close marked and adjusted prices approximating cash distributions. The results showed a mean of -0.4898687579872285% and a worst of -2.445854225070998%. The conclusion is that this is only a baseline and does not validate stop, target, or trail, and a profitable holdout does not repair a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs were retained.
The following premises were used: the Protocol 596/598 was frozen before prices were considered, and the Relative Strength Index (a measure of stock price movement) 2 declines for 3 days starting below 65, with the close being greater than the Simple Moving Average (a stock price average) 200. The entry and exit points were determined by the following open after the Relative Strength Index was greater than 75, with no overlap. A chronological sample was used as a holdout, with the years 2016-2020 used for training and 2021-2025 used as a holdout, and 2026 considered recent. No parameters were fitted, but the market period was previously inspected. An explicit cash long only vector was used, with a 5 basis point fee and a 5 basis point slip each way, reconciling independent wealth. Daily close was marked to the decimal day, and adjusted prices approximated cash distributions. The results showed a mean of 0.07524363740623041% and a worst loss of -8.519103138748662%. The conclusion is that this is only a baseline, and does not validate stop, target, or trail strategies. A profitable holdout does not make up for a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected for profit, and all five ETFs were retained.
A trading plan for EFA has been made. It is based on three premises. First, the plan uses a method called Protocol 596/598, which is frozen before prices are considered, and it looks at something called the RSI2, which is a measure of how fast prices are changing, and it must have been declining for 3 days and be below 65, and the closing price must be higher than the SMA200, which is the average price over 200 days. The plan also looks at when to enter and exit the market after the RSI is higher than 75, with no overlap. Second, the plan uses a sample of data from 2016 to 2020 for training and from 2021 to 2025 for testing, with 2026 being the most recent data, and it does not use any parameters that are fitted to the data, but it does look at the market period beforehand. Third, the plan uses a long-only strategy, which means it only buys, and it uses a fee of 5 basis points and a slip of 5 basis points each way, and it reconciles the wealth independently and marks the daily close as DD, and it adjusts the prices to approximate the cash distributions. The results show a mean of -0.10215037465943766% and a worst result of -8.944736735083858%. The conclusion is that this is only a baseline and does not validate stop, target, or trail, and a profitable test period does not make up for a negative earlier period or justify entering the market. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected to make a profit, and all five ETFs were kept.
The automated trading fund manager made the following premises: the protocol 596/598 was frozen before prices were considered, and the Relative Strength Index (a measure of stock price movement) 2 declines for 3 days starting below 65, with the close price being greater than the Simple Moving Average (a measure of stock price trend) 200. The manager also considered the recent chronological sample, which includes data from 2016 to 2020 for training and 2021 to 2025 for holdout, and 2026 for recent data, with no fitted parameters but previously inspected market periods. The manager used an explicit cash long only vector with a 5 basis point fee and 5 basis point slip each way, which reconciles independent wealth, and daily close was marked as DD, with adjusted prices approximating cash distributions. The results showed a mean of 1.272982569226051% and a worst loss of -0.05446169379021981%. The conclusion is that this is a baseline only and does not include stop, target, or trail validation, and a profitable holdout period does not make up for a negative earlier period or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No sample or parameter was selected for profit, and all five ETFs were retained.
This report is based on premises that include a protocol with specific rules. The rules are: the Relative Strength Index, or RSI, a measure of how quickly a stock's price is changing, declines for 3 days starting below 65, and the stock's closing price is higher than its Simple Moving Average, or SMA, over 200 days. The report also includes a holdout, which is a sample of data set aside to test the protocol, from the years 2021 to 2025. The data is adjusted to account for cash distributions and fees. The results show a mean return of 0.9381751690784071% and a worst return of -6.804647419897636%. The conclusion is that the holdout period was profitable, but this does not make up for earlier losses or justify entering the market. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were chosen to maximize profit, and all five ETFs were kept.
This is a description of a trading strategy for the IWM fund. The strategy is based on three premises: first, certain rules are applied before prices are considered, including a decline in the RSI2 indicator for three days starting below 65, and the closing price being above the SMA200 indicator; second, the strategy is tested on a sample of data from 2016 to 2020, with a holdout period from 2021 to 2025, and a recent period in 2026; third, the strategy uses a cash long only approach with a 5 basis point fee and a 5 basis point slip each way. The strategy was tested on 39 samples, with a mean return of -0.6433249944791336% and a worst return of -30.15412515129472%. The conclusion is that this is only a baseline strategy and does not include stop, target, or trail validation. The strategy was profitable in the holdout period, but this does not make up for negative returns in earlier periods or justify entering the trade. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars and is based on an article from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
A trading strategy for the SPY stock has been analyzed. The strategy is based on three premises: first, a protocol that freezes prices before they are used, and looks for declines in the RSI2 indicator over three days, starting below 65, and a close above the SMA200 indicator, with entry and exit rules based on RSI levels above 75, with no overlap. Second, the strategy uses a recent chronological sample of data from 2016 to 2020 for training and 2021 to 2025 for holdout, with 2026 being the recent period, and no parameters were fitted to the data, but the market period was previously inspected. Third, the strategy uses a cash long only approach with a 5 basis point fee and 5 basis point slip each way, and reconciles independent wealth, with daily close marked and adjusted prices approximating cash distributions. The results show a mean return of 0.8809568246536165% and a worst return of -1.797301171955723% over 11 periods. The conclusion is that this is only a baseline strategy and does not include stop, target, or trail validation, and a profitable holdout period does not make up for negative earlier periods or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected for profit, and all five ETFs were retained.
A trading strategy for the SPY stock has been analyzed. The strategy is based on three premises: first, a set of rules including a decline in the RSI2 indicator over three days starting below 65, and the stock's close price being above its SMA200, with entry and exit points determined by the RSI indicator crossing 75, with no overlap. Second, the strategy uses a holdout sample, which is a chronological sample of data from 2016 to 2020 for training, 2021 to 2025 for holdout, and 2026 for recent data, with no parameters fitted to the data, but the market period was previously inspected. Third, the strategy uses a cash long only vector with a 5 basis point fee and 5 basis point slip each way, which reconciles independent wealth, with daily close marked and adjusted prices approximating cash distributions. The results show a mean return of 0.7162919775054395% and a worst return of -5.606497085752837% over 39 periods. The conclusion is that this is only a baseline test, not a validation of stop, target, or trail strategies, and a profitable holdout period does not make up for negative earlier periods or justify entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
A trading strategy for the SPY fund is based on three premises. First, the strategy uses a protocol with specific rules: it looks at the Relative Strength Index, or RSI, which measures how quickly a stock's price is changing, and only considers trades when the RSI is below 65 and the closing price is higher than the Simple Moving Average, or SMA, of the last 200 days. It also considers trades after the RSI goes above 75, without overlapping trades. Second, the strategy uses a chronological sample of data from 2016 to 2020 for training and from 2021 to 2025 for testing, without adjusting any parameters. Third, the strategy uses a cash long only approach, which means it only buys stocks, with a 5 basis point fee and a 5 basis point slip, and reconciles independent wealth. The strategy was tested on the SPY fund with 39 trades, resulting in a mean return of -0.4071933868780629% and a worst return of -22.67570559906531%. The conclusion is that this strategy is only a baseline and does not validate stop, target, or trail rules. The strategy was profitable in the test period but did not make up for earlier losses. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars and the original strategy is from https://tradingmarkets.com/recent/the_improved_r2_strategy_84_correct_with_just_6_rules_-674361. No samples or parameters were selected to maximize profit, and all five ETFs were retained.
A news scan found the headline 'SOL/USD 1Hour train-selected trend/volatility rule fails chronological holdout'. The automated trading fund manager used a set of rules to decide when to buy or sell the cryptocurrency SOL/USD. These rules were based on the price of SOL/USD over different time periods, such as 20, 50, or 100 days, or 24, 72, or 168 hours. The manager also considered the volatility of the price, which is a measure of how much the price changes over time, referred to as 'rv20' and 'trailing120median'. The rules were tested on data from January 8, 2025, to September 7, 2026. The results showed that the rules did not work well, with a return of -67.79% during the training period and -48.84% during the testing period. The manager also considered the effects of fees, which are costs associated with buying or selling, and slippage, which is the difference between the expected price and the actual price. The fees were 0.25% and the slippage was 0.05% for each trade. The manager stressed the results by adding 0.50% and 0.10% to the fees and slippage. The results were recorded in a table with six settings. The manager concluded that the rules were not valid and should not be used to allocate funds. The validity of the results is limited by the fact that the data was recycled and the market history was incomplete. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the cost of the data is https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about a SOL/USD 1Day train-selected trend/volatility rule that fails chronological holdout. The rule uses a grid with daily or hourly time frames, comparing closing prices to a simple moving average, or SMA, which is a calculation of the average price of a stock over a certain period of time. The rule also considers volatility, which is a measure of how much the price of a stock changes. The rule was trained on data from June 18, 2025, to January 4, 2026, and tested on data from January 4, 2026, to September 6, 2026. The results show that the rule had a return of 0.44% during training, but a return of -22.64% during testing. The rule also had a drawdown, which is a decline in value, of 34.30% during training and 46.99% during testing. The stress test, which simulates difficult market conditions, showed a return of -28.45% and a drawdown of 50.53%. The results are recorded with six different settings, but no selection was made based on the holdout period. The conclusion is to reject allocation to this rule. The validity of the results is limited by the use of recycled market history and inconsistent daily and hourly opening prices. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the cost is https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about an automated trading fund manager's notes. The manager used a method called a frozen grid with daily and hourly time frames to make decisions about buying and selling the cryptocurrency Ethereum, or ETH, in US dollars, or USD. This method is also known as a trend-following strategy, which is a way of making investment decisions based on the direction of the market. The manager compared the current price of ETH to its average price over a certain period, known as a simple moving average, or SMA, which is a calculation of the average price of a security over a certain period of time. If the current price was higher than the average price, the manager would buy ETH. The manager also used a concept called vectorbt fees, which refers to the costs of using a certain trading platform, and slippage, which is the difference between the expected price of a trade and the actual price. The manager trained the strategy on data from the first 60% of a certain time period, starting from January 8, 2025, and then tested it on the remaining data, from January 4, 2026, to September 7, 2026. The results showed that the strategy did not perform well, with a return of -73.65% during the training period and -56.88% during the testing period. The manager also tested the strategy under stressful conditions, with a return of -78.88%. The manager concluded that the strategy was not valid and should not be used for investment decisions. The data used for the test came from a website called runway.adityaperswal.workers.dev and the costs of trading came from a website called docs.alpaca.markets.
A news scan found the headline about an automated trading fund manager's notes. The manager used a trend and volatility rule to make decisions about buying and selling Ethereum, which is a type of cryptocurrency, denoted as ETH, in US dollars, denoted as USD. The manager looked at daily and hourly price changes over different time periods, such as 20, 50, and 100 days, or 24, 72, and 168 hours, to decide when to buy or sell. The manager also considered the average price over a certain period, known as a Simple Moving Average, or SMA, and a measure of price volatility, known as rv20. The manager used a type of analysis called vectorbt, which includes fees of 0.25% and slippage of 0.05% for each buy and sell, and stress testing with an additional 0.50% and 0.10% added. The manager trained the model on the first 60% of the data, starting from June 18, 2025, after a 168-hour warm-up period, and selected the model with the highest return. The chosen model used a 100-day window and a volatility filter. The manager then tested the model on the remaining data from January 4, 2026, to September 6, 2026. The results showed a return of 64.26% during the training period, but a loss of 4.70% during the testing period. The manager also reported a drawdown, which is the largest peak-to-trough decline, of 17.80% during the training period and 20.48% during the testing period. The manager concluded that the model did not perform well and rejected allocating funds to it. The manager noted that the results are limited by the use of recycled market history and missing hourly data, and that the test was a diagnostic chronological out-of-sample test, not an untouched discovery out-of-sample test. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and the cost information is https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'BTC/USD 1Hour train-selected trend/volatility rule fails chronological holdout'. The automated trading fund manager used a set of rules to decide when to buy or sell Bitcoin, based on its price over time. The rules used a 'frozen grid' of time periods, including daily and hourly windows, to make decisions. The manager also used a 'baseline' and 'rv20' to compare the price to its average over the past 20 days, and a 'trailing 120 median' to smooth out the price. The manager only bought Bitcoin when its price was above its average, and sold when the price was below. The manager also used'vectorbt fees' of 0.25% and'slippage' of 0.05% on each trade, and added a'stress' test of 0.50% plus 0.10% to simulate real-world conditions. The manager trained the rules on the first 60% of the data, after a 168-bar warm-up period, and selected the rules that gave the highest return. The chosen rules used a 168-hour window and a 'volfilter' to filter out small price changes. The manager tested the rules on the remaining data, from 2026-01-04 13:00:00+00:00 to 2026-09-07 05:00:00+00:00. The results showed a return of -67.23% during the training period, and -53.28% during the testing period. The manager also tested the rules under'stress' conditions, and found a return of -77.66%. The manager concluded that the rules did not work well, and rejected allocating funds to them. The manager noted that the results were limited by the use of recycled market history, and missing or inconsistent data. The data was sourced from https://runway.adityaperswal.workers.dev/data/crypto-bars, and the costs were based on https://docs.alpaca.markets/us/docs/crypto-fees.
A trading rule for Bitcoin to US Dollar exchange rate failed to perform well in a test period. The rule used a grid of daily and hourly time frames, with conditions such as closing price above a moving average, to decide when to buy or sell. The rule was trained on data from June 18, 2025, to January 4, 2026, and tested on data from January 4, 2026, to September 6, 2026. The test results showed a return of -8.767%, with a maximum loss of 13.518%. The rule was also stressed with additional costs and slippage, resulting in a return of -9.855%. The results were compared to a buy-and-hold strategy, which had a return of -11.912%. The conclusion is to reject allocating funds to this rule. The test was limited by the use of recycled market history and inconsistent data. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/crypto-bars and the cost information to be https://docs.alpaca.markets/us/docs/crypto-fees.
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.