Stats

A trading strategy was tested on the stock APD from January 3, 2022, to September 8, 2026. The strategy, which is a type of momentum trading, was trained on data from 2022 to 2025 and then tested on untouched data from 2026. The strategy entered a trade when the closing price was higher than the prior 20-day closing high, and exited 10 sessions later. The strategy did not allow overlapping trades and excluded incomplete final trades. The results showed a return of -5.33%, a drawdown of 13.04%, and a win rate of 28.57%. The average win was 2.77% and the average loss was 2.14%. The strategy used a 10 basis point fee on each side of the trade, plus 30 basis points of slippage, which is a type of trading cost that occurs when a trade is not executed at the expected price. The data used for the strategy came from a source that provides adjusted stock prices, which is a type of price adjustment that accounts for certain market factors. The results are based on a small sample size of 7 trades, and the strategy is not suitable for actual orders due to the uncertainty of daily returns and the lack of modeling for 15-minute stops. The Quarter Kelly criterion, which is a measure of risk, was 0, and the actual allocation was 0. The strategy was not refit to the holdout data, and the adjusted history is a research proxy, not a raw-stop simulation.

A trading strategy was tested on the stock APD from January 3, 2022, to September 8, 2026. The strategy, which is a type of momentum strategy, or a way to follow the direction of a stock's price, was trained on data from 2022 to 2025 and then tested on untouched data from 2026. The strategy entered a trade when the stock's closing price was higher than its highest closing price in the past 20 days, and exited 10 sessions later. The strategy did not allow overlapping trades and excluded any trades that were not complete. The results showed a return of -2.65%, a drawdown of 11.28%, a win rate of 42.86%, an average win of 2.15%, and an average loss of 2.20%. The strategy was tested using a type of backtesting software called vectorbt, which uses the opening price to execute trades and the closing price to mark them, and includes fees of 10 basis points, or 0.1%, on each side of the trade, plus 10 basis points of slippage, or the difference between the expected price and the actual price. The data used for the test came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all. The results also showed a mean return of -0.34% and a quarter Kelly, a measure of the optimal fraction of wealth to bet, of 0. The strategy is not suitable for actual orders due to the small sample size and unmodeled 15-minute stops. The allocation to this strategy is 0, and there will be no refit from the holdout data.

A trading strategy was tested on LIN stock from January 3, 2022, to September 8, 2026. The strategy, a type of momentum proxy, used daily price adjustments to decide when to buy and sell. It entered a trade when the closing price was higher than the prior 20-day high, and exited 10 sessions later. The test included fees of 10 basis points, or 0.1%, on each buy and sell, plus 30 basis points of slippage, which is a type of trading cost. The results showed a return of -8.55%, a drawdown of 17.34%, and a win rate of 37.5%. The average winning trade was 2.27%, and the average losing trade was -3.08%. This strategy is not suitable for actual trading orders due to the small sample size and uncertainty of daily returns. The Quarter Kelly, a measure of optimal bet size, was 0, which means no allocation was made. The adjusted history used in this test is a research proxy, not a raw simulation of stop losses.

A trading strategy was tested on LIN stock from January 3, 2022, to September 8, 2026. The strategy, which is a type of momentum trading, was trained on data from 2022 to 2025 and then tested on untouched data from 2026. The strategy entered a trade when the stock's closing price was higher than its highest closing price in the previous 20 days, and exited 10 sessions later. The strategy used a 10 basis point fee for each trade, plus a 10 basis point slippage, which is a type of trading cost. The results showed a return of -5.58%, a drawdown of 15.67%, and a win rate of 37.5%. The average winning trade was 2.68%, and the average losing trade was 2.69%. The strategy was tested 8 times, with an average return of -0.68% per trade. This strategy is not suitable for actual trading due to its high uncertainty and unmodeled 15-minute stops. The Quarter Kelly criterion, a measure of risk, was 0, which means the strategy did not meet the risk threshold. As a result, the actual allocation to this strategy was 0. The adjusted history used in this test is a research proxy, not a raw simulation of stop losses.

A diagnostic test was executed on September 9 at 02:04 using the node /app/agent/dist/backtest.js program with the symbols SEI, LEU, and HUT, and a breakout strategy. The test looked back 20 periods over 730 days, with a stop loss of 3 percent and a target of 6 percent. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The results for HUT were: 501 bars of data, a return of -38.57 percent, a buy and hold return of 899.20 percent, a maximum drawdown of 48.15 percent, a win rate of 23.21 percent, and an average trade return of -0.80 percent, based on a sample of 56 trades. This was a same-close IEX diagnostic test without considering the cost of holding, and the strategy was not validated. Separate tests, coded independently, were run from 1312 to 1322 to define different exits, but this does not claim to replicate the results.

A trading test was done on September 9 at 02:04 using a computer program called backtest.js. The test looked at the stocks SEI, LEU, and HUT using a strategy called breakout, which means buying when a stock's price breaks out of a certain range. The test looked back 20 days and considered 730 days of trading. It used a stop loss of 3 percent and a target profit of 6 percent. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The results for LEU were: 501 trades, a return of 16.72 percent, a buy and hold return of 418.07 percent, a maximum loss of 26.54 percent, a win rate of 39.58 percent, and an average trade return of 0.40 percent, based on 48 samples. This test did not consider costs and did not validate the strategy. Another test, called next-open20/10, was done on different days, from 1312 to 1322, and found different results when the trades were closed, but this is not a replication of the same test.

A diagnostic test was executed on September 9 at 02:04 using the node /app/agent/dist/backtest.js program with the symbols SEI, LEU, and HUT, and a breakout strategy. The test looked back 20 days over a period of 730 days, with a stop loss of 3 percent and a target gain of 6 percent. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The results for SEI showed 501 bars, a return of -16.316623579462195 percent, and a buy and hold return of 465.4288240495137 percent. The maximum drawdown was 24.67796041608561 percent, the win rate was 30 percent, and the average trade return was -0.37902855640591604 percent, based on a sample of 40 trades. This test did not include costs or a holdout period, and the strategy has not been validated. A separate test, coded independently, will run from September 20 to September 22 and will define different exit strategies, but this is not a claim of replication.

A trading strategy for LIN was tested using historical data from 2021 to September 8, 2026, with adjustments made for historical SIP. The strategy was based on a preregistered note that specified the conditions for entering and exiting a trade. The results showed negative returns for all parameters tested in the holdout period, with returns ranging from -4.056846414245119% to -10.004885943988047%. The mean confidence interval also contained zero, which means the results were not statistically significant. Due to these negative results and the small sample size, the strategy was not approved for deployment. Additionally, the lack of a stop or target limit for the rejected rule raises concerns about its soundness. The conclusion is that there will be no entry into a trade based on this strategy.

A trading strategy for OESX was tested using historical data from 2021 to September 8, 2026, with adjustments made for historical stock prices. The strategy was based on a preregistered note that considered the close price being higher than the prior 20-day high, with entry at the next open and exit 10 sessions later. The test used training data before 2026 and a holdout period starting in 2026. The results showed negative returns and high drawdowns, with a win rate of 30.30% and an average win percentage of 11.46%. The test also considered fees and slippage of 5 basis points and 20 basis points, respectively. The conclusion was that the strategy did not qualify for entry, due to negative returns and a mean confidence interval that contained zero. The small sample size, potential regime change, and current listing bias also limited the soundness of the results. Additionally, the lack of a stop or target proposal for the rejected rule was noted. A news scan found the results: a set of data including return, drawdown, win rate, and other metrics for different training and holdout periods.

A trading strategy for the stock ALM was tested. The strategy was based on two premises. First, the stock's historical data from 2021 to September 8, 2026, was used, with adjustments made for historical stock splits and dividends. The data was obtained from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=ALM&timeframe=1Day&feed=sip&adjustment=all. Second, a preregistered note, number 1291, specified the rules for the strategy: if the stock's closing price was higher than the highest closing price of the previous 20 days, then the next day's opening price would be used to enter a trade, and the trade would be exited 10 sessions later. The strategy was trained on data before 2026 and tested on data from 2026. The results showed that the strategy had a return of 76.73% in training, but only 35.24% in the holdout period. The strategy also had a high drawdown of 29.35% in training and 34.44% in the holdout period. The win rate was 100% in training, but only 85.71% in the holdout period. The mean percentage return was 20.98% in training, but only 5.90% in the holdout period. The average win percentage was 20.98% in training, but only 11.67% in the holdout period. The average loss percentage was 0% in training, but 28.73% in the holdout period. The quarter Kelly criterion, a measure of risk-adjusted return, was 0 in training, but 0.1263 in the holdout period. The mean confidence interval percentage was between -7.82% and 16.67% in the holdout period. The simulation showed a 7.69% probability of loss and a median return of 11.78% in the holdout period. The 95th percentile drawdown was 9.14% in the holdout period. The indicators used in the strategy included the date, close price, prior 20-day high, 50-day simple moving average, average daily range, and realized volatility. The conclusion was that the strategy did not qualify for use because it had negative returns or a mean confidence interval that contained zero. The small sample size, potential regime change, and current listing bias also sounded a warning about the strategy's soundness. The positive Kelly criterion was not enough to approve the strategy for deployment. No stop or target was proposed for the rejected rule.

A trading strategy for LPTH was tested using historical data from 2021 to September 8, 2026. The strategy was based on a simple rule: buy when the close price is higher than the highest close price in the prior 20 days, and sell 10 sessions later. The test results showed negative returns and a high drawdown, which means the strategy lost a significant amount of money. The results were: a return of -31.45% to -49.83% in the training period, and -44.43% to -47.03% in the holdout period. The win rate was 38.46% in the training period and 33.33% in the holdout period. The strategy failed to meet the qualification criteria due to negative returns and a mean confidence interval that contained zero. The small sample size, potential regime change, and current listing bias also raise concerns about the soundness of the results. Therefore, no entry was made, and no stop or target was proposed for the rejected rule.

A trading rule for VIAV was tested. The rule was based on two premises. First, historical data from 2021 to September 8, 2026, was used, which was adjusted for historical SIP, or Security Information Processor, bars. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=VIAV&timeframe=1Day&feed=sip&adjustment=all. Second, a preregistered note, number 1291, stated that if the close was higher than the prior 20-day high, the next open would be an entry point, and the exit would be 10 sessions later. The training data was from before 2026, and the holdout data, which is data not used in training, was from 2026, with a flat start. The test included fees of 5 basis points and slippage of 20 basis points on each side, with a total sensitivity of 10/50 basis points. The results were as follows: for a training set with a parameter of 0.001, the return was -21.56101728254379%, the drawdown was 43.87644573960039%, and the win rate was 54.54545454545454%. For a parameter of 0.0025, the return was -31.260719711185136%, the drawdown was 47.566238926226866%, and the win rate was 45.45454545454545%. For a parameter of 0.005, the return was -44.8355699210074%, the drawdown was 55.98419651480479%, and the win rate was 43.18181818181818%. In the holdout set, for a parameter of 0.001, the return was 39.57439740577789%, the drawdown was 31.49954356238619%, and the win rate was 75%. For a parameter of 0.0025, the return was 36.26448391220976%, the drawdown was 31.909316470071737%, and the win rate was 75%. For a parameter of 0.005, the return was 30.921419031791288%, the drawdown was 32.58683756305105%, and the win rate was 75%. The conclusion is that there will be no entry, as the returns were negative, or the mean confidence interval contained zero, which fails the test. No parameter was chosen from the holdout set. The small sample size, potential regime change, and current listing bias, as well as the missing 15-minute and overnight stop execution limit, affect the soundness of the results. A positive Kelly point is not enough to approve deployment. No stop or target was proposed for the rejected rule.

A news scan found the headline 'FCX frozen next-open breakout fails qualification'. Premises for this conclusion are: 1. Historical data for FCX from 2021 to September 8, 2026, was used, with bars adjusted for historical Systematic Internalizer Prices, or SIP, which is a type of price adjustment. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FCX&timeframe=1Day&feed=sip&adjustment=all. 2. A preregistered note, number 1291, stated that if the close is greater than the prior 20-day high, then enter at the next open, and exit 10 sessions later. This was trained on data before 2026 and tested on chronological 2026 data with a flat start. Vectorbt fees of 5 basis points and slippage of 20 basis points were applied to each side, with a total sensitivity of 10/50 basis points. 3. The results were: for a training set with a parameter of 0.001, the return was -16.445877910652634, the drawdown was 55.37675849852162, and the win rate was 47.61904761904761. For a parameter of 0.0025, the return was -26.33746169870869, the drawdown was 57.975418192935116, and the win rate was 47.61904761904761. For a parameter of 0.005, the return was -40.290445910765285, the drawdown was 63.87768145526771, and the win rate was 47.61904761904761. For the holdout set with a parameter of 0.001, the return was 27.88240266538614, the drawdown was 23.47485665424227, and the win rate was 57.14285714285714. For a parameter of 0.0025, the return was 25.22486932262869, the drawdown was 23.704088465794026, and the win rate was 57.14285714285714. For a parameter of 0.005, the return was 20.917765161436925, the drawdown was 24.08462011911663, and the win rate was 57.14285714285714. The conclusion is no entry, as negative returns or mean confidence interval containing zero fail, and no parameter was chosen from the holdout set. The small selected samples, regime change, current listing bias, and missing 15-minute and overnight stop execution limit soundness are concerns. A positive Kelly point is not approval for deployment. No stop or target was proposed for the rejected rule.

The trading strategy for CW failed to qualify. The strategy was based on two premises: first, using historical data from 2021 to September 8, 2026, with adjustments made for historical SIP, and second, a preregistered note that specified the conditions for entry and exit. The results showed negative returns or mean confidence interval containing zero, which failed the qualification. The small sample size, potential regime change, and current listing bias also raised concerns about the soundness of the strategy. Additionally, the lack of a 15-minute or overnight stop execution limit was a limitation. Since no parameter was chosen from the holdout, no entry was made. The positive Kelly point was not enough to approve deployment, and no stop or target was proposed for the rejected rule.

A trading strategy for HUT was tested. The strategy was based on two premises: 1. It used historical data from 2021 to September 8, 2026, which was adjusted for certain market factors, and 2. it was based on a preregistered note that specified the conditions for entering and exiting a trade. The results of the test were as follows: the strategy had a return of 250.58% in training, with a drawdown of 58.39% and a win rate of 56.41%, but in the holdout period, the return was 13.96% with a drawdown of 33.46% and a win rate of 50%. The strategy also had a quarter Kelly criterion of 0.0659 in training and 0.0539 in the holdout period. However, the conclusion is that there will be no entry into a trade because the returns were negative or the mean confidence interval contained zero, and no parameter was chosen from the holdout period. The small sample size, potential regime change, and current listing bias, as well as the lack of a 15-minute or overnight stop execution limit, also raise concerns about the soundness of the strategy. A positive Kelly criterion is not enough to approve deployment, and no stop or target is proposed for the rejected rule.

A trading rule for CCJ was tested. The rule was based on two premises: 1. Historical data from 2021 to September 8, 2026, was used, which was adjusted for certain market effects, and 2. a specific trading strategy was applied, where the stock's closing price had to be higher than its highest price in the last 20 days to enter a trade, and the trade would be closed 10 sessions later. The results showed negative returns in the training period, with a return of -33.31% for one set of parameters and -43.28% for another, and a drawdown of 67.90% and 71.23% respectively. In the holdout period, the returns were 12.09%, 10.76%, and 8.56% for different parameters, but the sample size was small. The trading rule also had a high drawdown and a low win rate. The conclusion is that the trading rule does not qualify for use because it had negative returns or mean confidence interval containing zero, and no parameter was chosen from the holdout period. Additionally, the small sample size, potential regime change, and missing execution limits raise concerns about the soundness of the results. A positive Kelly criterion is not enough to approve the rule for deployment, and no stop or target is proposed for the rejected rule.

A trading strategy for LEU was tested based on two premises. First, historical data from 2021 to September 8, 2026, was used, with adjustments made for historical SIP, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LEU&timeframe=1Day&feed=sip&adjustment=all. Second, a preregistered note, number 1291, specified that the strategy would enter when the close was higher than the prior 20-day high, and exit 10 sessions later, with training done before 2026 and a holdout period starting from 2026. The strategy also accounted for fees and slippage. The results showed returns of 413.28, 349.81, and 260.98 for different training parameters, but negative returns of -32.42, -33.63, and -35.59 in the holdout period. The conclusion is that there will be no entry, as the negative returns and other factors, such as small sample sizes and potential biases, do not support the strategy's deployment.

A trading strategy for SEI was tested using historical data from 2021 to September 8, 2026. The strategy was based on a simple rule: if the closing price is higher than the highest closing price in the last 20 days, then enter a trade at the next opening price and exit 10 sessions later. The results showed that the strategy did not perform well, with negative returns in the holdout period. The mean return was -47.99657328093197% for a parameter of 0.001, -48.924262336320574% for 0.0025, and -50.43379499256859% for 0.005. The win rate was also low, at 16.666666666666664%. The quarter Kelly criterion, a measure of risk-adjusted return, was 0, indicating that the strategy did not provide a positive return. Due to the poor performance, no entry was recommended. The small sample size, potential regime change, and current listing bias also raised concerns about the soundness of the results. Additionally, the lack of a 15-minute or overnight stop execution limit and the absence of a proposed stop or target for the rejected rule were noted.

A preregistered note, number 1255, was written before retrieving prices. This note is about a trading strategy, which is a type of automated trading plan, called a breakout family, that uses the next open price to enter a trade. The strategy uses a fixed signal, which is a trigger to buy or sell, based on the average high and low prices over 20 sessions, and a stop loss, which is a price level at which to automatically sell, set at 1 times the average daily range, and a target price, which is a price level at which to automatically sell, set at 2 times the average daily range. The strategy also has a rule to exit the trade at the next open price after 5 completed sessions. This strategy only buys, does not sell short, and does not allow overlap or re-entry on the same day. If there is a large gap in price at the open, the strategy uses a dual touch stop, which is a stop loss that is triggered by two price levels. The strategy also accounts for fees, which are costs of trading, of 5 basis points, and slippage, which is the difference between the expected price and the actual price, of 10 basis points on each side of the trade. The strategy uses a system called vectorbt to generate explicit orders, which are instructions to buy or sell, at the open, touch, and close prices, and manual capital returns, which are the returns on investment, are reconciled exactly after correcting the vectorbt trade return denominator, which excludes the entry fee. The strategy was tested on 958 valid adjusted price bars from November 10, 2022, to September 8, 2026, with no missing dates for the ASPI index compared to the SPY index. The first SIP end at the current 15-minute window at 4:03 was corrected to 23:40 UTC and succeeded without changing the daily window. The strategy was trained through 2024 and resulted in a return of -51.7294% with 46 trades. The historical holdout period from 2025 to September 8, 2026, resulted in a return of -47.6615% with a drawdown, which is the peak-to-trough decline, of 61.0281% and 24 trades, with 5 wins and 19 losses. The average win was 16.4566% and the average loss was 7.1155%, with a mean return of -2.20465% and a quarter Kelly, which is a measure of risk, of 0. The double cost return was -51.2974%. Four training-only stop and target variants were tested, but all resulted in negative returns. The default strategy was retained and no further tuning was done. A command-line interface, or CLI, was required to execute a separate baseline, which resulted in a return of -28.2872% with 35 trades and a drawdown of 46.2699%. The CLI lacks next open, cost, and 15-minute controls, and is not eligible for trading. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. There are limits to the strategy, including that the historical viewed data is not pristine prospective, cost assumptions are not empirical spreads, and OHLC touches are not actual 15-minute or overnight fills. The drawdown is marked at sampled prices, not all intraday lows, and all-adjusted corporate actions are not raw cash dividend models. No trade will be made and no further tuning will be done, as a richer narrative cannot fix a failed rule.

A computer program executed a test on the stock MATX using a breakout strategy. The test looked back 20 days over a period of 1700 days, with a stop loss of 3% and a target gain of 4.5%. The test resulted in 1165 trades, with a return of -30.82459%, a maximum drawdown of 41.17376%, and a win rate of 28.86598%. The average trade return was -0.35229%, compared to a buy and hold return of 158.23066%. The test used data from https://runway.adityaperswal.workers.dev/data/stock-bars. This test is different from a previous test that used a next-open rule. The results of this test should not be directly compared to the previous test, and the difference in results should not be attributed solely to the data feed used. The test was run as a diagnostic and the output should be preserved. The test did not hold out any data, but a separate test will be run with holdout data for 2026.

A preregistered note, number 1013, analyzed the stock MATX using a consolidated SIP adjustment on daily bars from 2021 to September 4, 2026, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=MATX&timeframe=1Day&feed=sip&adjustment=all. The analysis was divided into training from 2022 to 2024, validation in 2025, and a holdout period from January 1 to September 4, 2026, with a reset to flat. The strategy was to go long on the next open if the close was greater than the previous 20-day high and the 50-day simple moving average, which is a calculation of the average price of a stock over a 50-day period. The stop loss was set at 1 times the average daily range of the previous 20 days, and the target was set at 1.5 times this range. The maximum number of sessions was 10. Entry day exits were included in two daily execution slots, with a gap stop paid at the open and a gap target capped at the target, both of which were touched intraday, and no same-day reentry was allowed. Fees were 5 basis points plus slippage of 10 basis points on each side, with a stress test of 25 basis points slippage. The results were reconciled using vectorbt, a backtesting library, which is a tool used to test trading strategies, and an independent ledger, with a daily marked drawdown, which is the maximum loss from a peak to a trough, not 15-minute venue execution. The training results showed a return of 12.7051%, a drawdown of 13.9755%, 38 trades, a win rate of 52.6316%, and an average win of 3.4027 and average loss of 2.9987. The validation results showed a return of 6.3149%, a drawdown of 2.977%, and 5 trades. The holdout results showed a return of -1.1617%, a drawdown of 10.7850%, 13 trades, a win rate of 46.1538%, and an average win of 3.4881 and average loss of 3.0562. The stress test results showed a return of -4.9422% and a drawdown of 13.6811%. A fixed baseline was not changed after training, with only a 9-neighbor sweep, which is a method of analyzing nearby data points. A correction to the code explicitly prioritized the gapped target before the later intraday stop, but the results were unchanged. There is no claim of causality or execution at 15-minute observations. The selected current ticker, small holdout, and corporate action cash and executable overnight quote gaps mean that a positive result alone would not qualify. In this case, the point edge also fails, and the allocation is zero. A news scan found the headline related to MATX, but it is not included here as it is not part of the original note.

A news scan found the headline 'CF 15-minute polling diagnostics lose with regular and available extended-hour observations'. The diagnostics for the stock CF show the following results: the system uses a 50-period simple moving average, or SMA50, which is a calculation to find the average price of a stock over a certain period. The system had 6676 15-minute bars of data from January 2 to September 4, 2026, with 4420 regular bars and 170 days of all 26 regular trading hours, or RTH, intervals. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CF&timeframe=15Min&feed=sip&adjustment=all. The system checks the last completed 15-minute close and exits when the stop or target is crossed, with a 10 basis points lippage, which is the difference between the expected price and the actual price, and a 5 basis points fee on each side. The results for regular trading hours only are: 16 trades, a return of -21.51943%, a drawdown of 28.64888%, a win rate of 31.25%, an average win of 5.24118%, and an average loss of 4.41431%. The results for available extended hours are: 16 trades, a return of -13.63352%, a drawdown of 26.42337%, a win rate of 37.5%, an average win of 4.94403%, and an average loss of 4.26073%. These diagnostics are not actual tests of the system on a trading venue and have some limitations, such as not including overnight hours and assuming zero latency, which is the delay between the time the system sends a trade and the time it is executed. The entry point for the system remains at zero.

A diagnostic test for the CF CLI breakout strategy failed. The test was run using the command node /app/agent/dist/backtest.js with the following parameters: symbols CF, strategy breakout, lookback 20, days 1700, stop percentage 3, and target percentage 4.5. The results showed 1165 trades using IEX bars, with a return of -37.16635%, a drawdown of 44.41383%, a win rate of 31.68317%, and an average trade return of -0.425359%. The data used for the test can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. This test is a full sample built-in diagnostic with its own signal exit rules and does not account for costs or out-of-sample qualification. A separate custom test using vectorbt includes costs, next open entry, ADR exits, and chronological holdout, but the results of these two tests should not be combined or used to claim that the CLI verifies actual venue fills.

A news scan found the preregistered notes 962/966. The notes are about a trading strategy that uses Python and vectorbt, which is a backtesting library, to make long-only trades, meaning it only buys stocks, not sells them short. The strategy uses a prior 20-high breakout, which means the stock price must be higher than its highest price in the last 20 days, and a 50-day simple moving average, or SMA, which is a measure of the stock's average price over 50 days. The strategy enters trades on the next open and sets a stop loss, which is a price at which to sell a stock to limit losses, at one times the average daily range, or ADR, and a target price at 1.5 times the ADR. The strategy also limits trades to 10 sessions and includes entry day exits, which means it can sell a stock on the same day it buys it. The strategy also accounts for gaps, which are large price movements, through the stop at open and target gaps capped at the target. Both the stop and target can be hit first. The strategy also includes fees of 0.05% and slippage, which is the difference between the expected price of a trade and the actual price, of 0.10% on each side of the trade. The period end is flat, meaning the strategy does not hold any stocks at the end of the period, and the benchmark is all-in cash, meaning it does not invest in any other assets. The notes also mention that an independent ledger agrees with the vectorbt compounded return and trade count to 1e-9, which is a very small number. The consolidated SIP adjustment is available at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CF&timeframe=1Day&feed=sip&adjustment=all. The strategy was trained on data from 2022 to 2024 and had a return of -8.2860%, with 30 trades, a maximum drawdown, or DD, of 17.7676%, and a win rate of 43.3333%. The strategy was validated on data from 2025 and had a return of +7.2461%, with 9 trades, a DD of 5.1629%, and a win rate of 55.5556%. The strategy was also tested on an untouched holdout set from 2026 and had a return of -22.3400%, with 17 trades, a DD of 26.9704%, and a win rate of 29.4118%. The average win was 4.11832% and the average loss was 3.71447%. The stock buy and hold return was +74.6333% in the holdout period, but this is not the strategy's expectancy, which is the expected return of the strategy. The strategy also loses 26.2014% in the holdout period and 16.1798% in the training period if the slippage is increased to 25 basis points, or 0.25%, each side. The current signal is false, meaning the strategy is not currently generating a buy or sell signal. The notes also mention that nine training-only lookback/SMA variants were reported separately, but the baseline was never selected from the holdout. The IEX baseline had a return of +4.6223% in the training period, +6.5787% in the validation period, and -25.2049% in the holdout period. The feed differences change the training signal, but not the decision. The daily adjusted OHLC, or open, high, low, and close, and mark-to-market DD are simplified, and polling and extended fills are evaluated separately. The adjusted dividends cannot certify live raw price stops. The current company selection and limited regimes constrain inference, and the rejection of this rule is not all CF strategies. The Quarter Kelly is 0, and there is no entry, meaning the strategy is not currently investing in any stocks.

A diagnostic test was run on a trading strategy using the command line interface. The test used a 20-day breakout strategy on the stock symbol GD, with a lookback period of 20 days, 1700 days of data, a stop loss of 5%, and a target profit of 8%. The data came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test used 1165 data points and made 83 trades, resulting in a return of -3.23388% and a maximum drawdown of 20.70207%. The win rate was 34.93976%. For comparison, a buy and hold strategy would have given a return of 69.6003%. This test was run to diagnose the strategy's performance and did not take into account any costs or fees. A separate test with costs recorded separately was also run. The command line interface used for this test is not trusted for actual trading or predicting political events.