Stats

This is a preregistered note, detailed in methods3577. The daily data used is from September 17, with training from 2016 to 2023, and a holdout period from 2024 to 2025. A separate diagnostic period is set for 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a next-open prior 20-close breakout and a 50-day simple moving average, which is a calculation of the average price of a stock over 50 days. The strategy also uses an arithmetic average true range, which measures the volatility of a stock, with brackets of 2 times the average true range below and 6 times above the average true range. The strategy has a 60-session maximum and includes entry-day stops and adverse opening gaps. The target opening priority is used, and ambiguous intraday stops are handled first. The Vectorbt backtesting framework is used with a $10,000 reinvested fractional proxy, a 1 basis point fee, and a 5 basis point slip per side. A stress test with a 15 basis point slip is also performed. The fixed brackets do not include 2R management, which refers to a risk management strategy, and actual 15-minute and overnight execution. There is no pre-2024 training history. The holdout period showed a marked return of -16.8897%, a closed compound return of -20.6682%, a maximum drawdown of 32.2133%, a win rate of 14.29%, 7 trades, 1 open trade, and 1 target. The forward period in 2026 showed a marked return of 122.8193%, a closed compound return of 112.7180%, a maximum drawdown of 10.9551%, a win rate of 83.33%, 6 trades, 1 open trade, and 5 targets. The stress holdout period showed a marked return of -18.1270%, a closed compound return of -21.7711%, a maximum drawdown of 32.9548%, a win rate of 14.29%, 7 trades, 1 open trade, and 1 target. Other trend filters, such as L10/40, were recorded as sensitivities but not selected by the holdout. The current survivor selection, retrospective adjustments, small sample size, prior viewing of history, and no historical primary-event labeling mean this is a technical overlay experiment, not proof of bottleneck alpha. The marked return includes open paper, while the closed statistics do not. The conclusion is that the short history remains unvalidated for allocation.

This is a preregistered note, detailed in methods3577. The daily data was collected from September 17, using 2016-2023 as the training period, which started on November 1, 2016, 2024-25 as the calendar holdout, and 2026 as a separate diagnostic period. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used a next-open prior 20-close breakout and a 50-day Simple Moving Average, with a 20-day arithmetic Average True Range, which is a measure of volatility, and brackets of 2 times the Average True Range below and 6 times above the Average True Range. The strategy also included entry-day stops, adverse opening gaps, and a target opening priority. The Vectorbt backtesting framework was used with a $10,000 reinvested fractional proxy, a 1 basis point fee, and a 5 basis point slip per side, which is a small cost added to each trade. The results for the training period were a marked return of 19.9143%, a closed compound return of 27.7017%, a maximum drawdown of 78.3639%, a win rate of 30.61%, 49 trades, 1 open trade, and 13 targets. For the holdout period, the results were a marked return of -25.6595%, a closed compound return of -25.6595%, a maximum drawdown of 40.4370%, a win rate of 20.00%, 10 trades, 0 open trades, and 2 targets. The forward 2026 results were a marked return of 90.8954%, a closed compound return of 76.5638%, a maximum drawdown of 11.9943%, a win rate of 57.14%, 7 trades, 1 open trade, and 4 targets. The stress holdout results were a marked return of -27.1315%, a closed compound return of -27.1315%, a maximum drawdown of 41.0297%, a win rate of 20.00%, 10 trades, 0 open trades, and 2 targets. The L10/40 and other trend filters were recorded as sensitivities but not selected by the holdout. The conclusion is that the baseline lacks consistent train and holdout support, so it is rejected as a standalone live rule.

This is a preregistered note, which means it was planned in advance, with detailed methods explained in note 3577. The trading strategy was tested on daily stock prices from September 2017, using data from 2016 to 2023 for training, 2024-2025 as a holdout period, and 2026 for diagnostic testing. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used a breakout method, which means buying when the stock price goes above a certain level, and also considered the average true range, or ATR, which is a measure of price volatility. The strategy also used moving averages, specifically the SMA50, which is the average price over the last 50 days. The strategy had rules for when to enter and exit trades, including stop-loss orders, which are used to limit losses. The results were tested using a simulated $10,000 investment, with a small fee of 1 basis point, or 0.01%, and a slip, or difference between the expected and actual trade price, of 5 basis points. The results showed a loss of 34.85% in the training period, and a gain of 7.03% in the holdout period. The strategy was also tested with a stress test, which simulates difficult market conditions, and the results showed a gain of 5.12% in the holdout period. The results also showed that the strategy had a drawdown, or maximum loss, of 77.56% in the training period, and 26.65% in the holdout period. The win rate, or percentage of successful trades, was 28.57% in the training period, and 33.33% in the holdout period. The number of trades, or N, was 42 in the training period, and 9 in the holdout period. The strategy was not selected based on trend filters, and the results are considered a technical overlay experiment, rather than proof of a successful trading strategy. The conclusion is that the baseline strategy lacks consistent support and should not be used as a standalone live trading rule.

This is a preregistered note, detailed in methods3577, using daily data from September 17, with training from 2016 to 2023, and a holdout period from 2024 to 2025. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a breakout system, which means buying when the price goes above a certain level, and a stop-loss, which means selling when the price falls below a certain level. The strategy also uses a moving average, which is a way to smooth out price fluctuations, and an Average True Range, or ATR, which is a measure of price volatility. The strategy was tested with $10,000 and a small fee of 1 basis point, or 0.01%, per trade, and a slip of 5 basis points, or 0.05%, per trade. The results were: a loss of 77.28% in the training period, a loss of 10.76% in the holdout period, and a gain of 85.92% in the forward period. The strategy also had a maximum drawdown, or loss, of 84.59% in the training period, and 41.51% in the holdout period. The win rate, or percentage of winning trades, was 21.57% in the training period, and 36.36% in the holdout period. The strategy was not selected based on trend filters, and the results are not conclusive due to the small sample size and other limitations. The conclusion is that the baseline strategy does not have consistent support and should not be used as a standalone live rule.

The automated trading fund manager analyzed a costed ATR breakout strategy with a calendar holdout. The manager preregistered a note with detailed methods. The analysis used daily data from September 2017, with a training period from 2016 to 2023, a holdout period from 2024 to 2025, and a separate diagnostic period in 2026. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used a next-open prior 20-close breakout and SMA50, with an arithmetic ATR, and E-2ATR/E+6ATR brackets, with a 60-session maximum. The manager included entry-day stops and adverse opening gaps, with a target opening priority and an ambiguous intraday stop first. The analysis used a Vectorbt $10,000 reinvested fractional proxy, with a 1bp fee and 5bp slip per side, and a stress test with a 15bp slip. The results showed a train period with a marked return of -68.7795%, a closed compound return of -68.9770%, a drawdown of 86.1382%, and a win rate of 19.57%. The holdout period showed a marked return of 13.3817%, a closed compound return of 13.3817%, a drawdown of 29.8290%, and a win rate of 37.50%. The forward 2026 period showed a marked return of 96.7260%, a closed compound return of 85.0282%, a drawdown of 22.0055%, and a win rate of 80.00%. The stress holdout period showed a marked return of 11.5820%, a closed compound return of 11.5820%, a drawdown of 30.2487%, and a win rate of 37.50%. The manager recorded L10/40 and other trend filters as sensitivities, but did not select them by holdout. The conclusion was that the baseline strategy lacked consistent train and holdout support, and was rejected as a standalone live rule.

The automated trading fund manager analyzed the 595SIP adjusted daily data from May 2024 to September 11. The strategy involved a breakout based on the prior close and a 50-period Simple Moving Average, with a stop loss and target set at 6 and 40 sessions, respectively. The manager also considered daily gap opens and low/high barrier stops. The screening proxy used was not the actual UTC manager, with a 2R and 15-minute execution delay, and no authorization was taken from daily fills. The training data showed a return of -17.147187% and a daily maximum drawdown of 28.310305% from the start to 2026, with a win rate of 20%. The historical half Kelly was clamped at 0. A trade from December 22 to January 13 was purged. The 2026 holdout data showed a return of 65.379540% and a daily drawdown of 17.874034%, with a win rate of 71.428571% and a stress of 63.080356%. The training data from L10 to N11 showed a return of -7.854358%, and from L40 to N7 showed a return of -0.917582%. No winning lookback was selected. The vectorbt long-only strategy showed a compounded return of 17.672193% over 595 bars, with a drawdown of 11.087333% and a win rate of 39.534884%. The current price of 25.13 is above the SMA of 21.4608, but the short training data was negative and there was a recent regime shift, so the current allocation was rejected and the 2026 win rate was not borrowed for Kelly.

A news scan found the headline 'R91308 fixed prior-close net3R gate: chronological manager-cadence replay'. This is a note from a trading fund manager. The manager uses a system that automatically makes trades. It looks for a 'breakout', which means when a stock's price moves outside a certain range, and also checks the 'SMA50', which is a moving average of the stock's price over 50 days. The system enters a trade when certain conditions are met, including the stock's price being below a certain level, and it only trades during 'RTH', which means regular trading hours on the New York Stock Exchange. The system also has rules for when to exit a trade, including after 40 sessions or when a certain profit is made. The manager tested the system using data from 2020 to 2024 and found that it made a return of 19.190992%, with a maximum drawdown of 49.046502%, and 36.3636% of trades were winners. The system was also stressed, which means it was tested in difficult market conditions, and it made a return of 14.060286%. The manager also tested the system on new data from 2025 and found that it made a return of 1.924385%, with a maximum drawdown of 15.627365%, and 36.3636% of trades were winners. The system made 1682 daily trades and 123209 raw trades, and it was found to be working correctly. The manager used data from a website, https://runway.adityaperswal.workers.dev/data/stock-bars, to test the system.

A news scan found the text of a trading fund manager's notes. The manager uses a system called cron_manager to make decisions. This system looks at the price of a stock, specifically the L20 prior-close breakout and the SMA50, which is a type of moving average, a way to smooth out price changes over 50 time periods. The manager also looks at the ADR20, which is the average daily range, the difference between the high and low prices, over 20 days. The manager uses this information to decide when to buy or sell a stock. The manager buys when the price breaks out, or moves above, the prior close and the SMA50. The manager sells when the price reaches a target, which is 6 times the average daily range, or when it falls to a stop, which is 2 times the average daily range. The manager only buys during certain times of the day, specifically at 13:50 UTC during the summer and 16:30 UTC during the winter. The manager also sells at specific times, 12:30, 13:50, 16:30, 19:40, and 21 UTC. The manager forces a sale on the 40th session, which is a trading period. The manager uses 15-minute and 5-minute price data to check the trades. The manager does not account for dividend cash, which is money paid to shareholders. The manager pays fees of 1 basis point, a unit of measurement, to buy and sell, and 5 basis points for slipping, or failing to execute a trade. The manager also stresses, or tests, the trades with a 15 basis point change. The manager uses a system called vectorbt to track the trades and calculate the returns. The manager trained the system from 2020 to 2024 and got a return of 36.189046%. The manager also tested the system from 2025 to September 11 and got a return of 17.407005%. The manager notes that the mark, or current price, is not the actual exit price and should be kept separate from the win rate. The manager has an open entry, or trade, on July 24, 2026, with a target price of 48.545919999999995. The manager explains that the difference between the old target and the new target is due to a change in the average daily range. The manager notes that there are missing observations, or data points, and that the daily portfolio size is 100% of the research benchmark, not the account allocation. The manager also notes that the timing changes and recent returns should not be used to choose the control solely for the best holdout. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the body of a report from an automated trading fund manager. The report describes a test of a trading strategy called R91306 TEN. The strategy uses a combination of technical indicators, including a prior-close breakout and a 50-period Simple Moving Average, or SMA50, which is a calculation of the average price of a stock over 50 trading days. The strategy also uses an Average Daily Range, or ADR, which is a measure of the average daily price movement of a stock. The test had specific rules, including a stop-loss order at 2 times the ADR and a target price at 6 times the ADR, with a maximum of 40 trading sessions. The test started at a fixed time, 13:50 UTC during summer and 16:30 UTC during winter, which are times when the New York Stock Exchange, or XNYS, is open for trading, known as Regular Trading Hours, or RTH. The test used real-time market data, including 15-minute and 5-minute opening prices, but the data was not always synchronous, meaning it was not always available at the same time. The test also took into account certain market events, such as stock splits, but did not account for dividend payments. The test included fees of 1 basis point, or 0.01%, per trade, and a slippage of 5 basis points, or 0.05%, which is the difference between the expected price of a trade and the actual price. The results of the test showed a return of 10.796047% from 2020 to 2024, with a maximum drawdown, or loss, of 72.455395%, and a win rate of 32.2581%. A separate test, called a holdout, from 2025 to September 11, showed a return of 17.542708%, with a win rate of 45.4545%. The holdout test also showed a stress, or risk, of 38.065430%, and a maximum drawdown of 17.094922%. The report notes that the results are based on a mark, or value, of 47.93 on September 11 at 19:55 UTC, which is not the actual exit price, but rather an estimate of the value of the portfolio at that time. The report also notes that the results are subject to certain limitations, including missing data and assumptions about trading activity. The data for the test came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars.

A trading fund manager tested a strategy called R91306 TEN open_manager. This strategy used a split-unit approach and costed chronological execution. It looked at the stock's prior close breakout and its 50-day simple moving average, or SMA50, which is a measure of a stock's average price over 50 days. The strategy also considered the stock's average daily range, or ADR, over 20 days. The test had a stop loss of 2 times the average daily range and a target of 6 times the average daily range, with a maximum of 40 trading sessions. The test started at 9:30 ET and had specific times to sell or adjust the trade. The test used 15-minute and 5-minute opening prices for checks and did not include dividend cash payments. The test also accounted for fees of 1 basis point per side and a 5 basis point slip, with a stress test of 15 basis points. The results of the test from 2020 to 2024 showed a return of 0.622574% and a maximum drawdown of 66.328898%. A separate test from 2025 to September 11 showed a return of 32.871082%. The current open position has a target of 48.365919999999996 as of July 24, 2026, at 13:30:00 UTC. The data for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars.

This is a test of a trading strategy called R91306 TEN. The strategy uses a combination of rules to decide when to buy or sell stocks. It looks at the stock's price at the end of the previous day and its average price over the last 50 days, which is called a Simple Moving Average or SMA. The strategy also considers the stock's price range over the last 20 days, which is called the Average Daily Range or ADR. The test started with a set of rules, including a stop-loss order at 2 times the ADR and a target price at 6 times the ADR, and a limit of 40 trading sessions. The test began at 9:30 ET and the strategy was set to sell at a 2.25% profit or at a break-even point every 15 minutes, without any human intervention. The test also included a rule to exit the trade on the 40th session. The data used for the test included 15-minute and 5-minute opening prices, which were not necessarily synchronous quotes. The test excluded half-days and after-hours trading sessions and did not account for dividend payments. The test also included fees of 1 basis point per side and a 5 basis point slippage, with a stress test of 15 basis points. The results of the test from 2020 to 2024 showed a return of -5.87%, a maximum drawdown of 66.47%, and a win rate of 41.38%. The results from 2025 to September 11 showed a return of 43.97% and a win rate of 66.67%. The test also included a mark-to-market calculation, which showed a return of 71.81% and a maximum drawdown of 18.83%. The mark is not the actual exit price, but rather an estimate of the value of the position at a given time. The test used data from the XNYS calendar and excluded dividend payments. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars.

A trading fund manager tested a strategy called R91306 TEN. This strategy used a method called a moving average, or SMA50, and a breakout, which is when a stock price moves past a certain level. The test also used something called an ADR, or average daily range, which is the average amount a stock price moves in a day. The test started at 9:30 ET and only automatic exits were allowed. The test used real-time data, but not all of it was synchronized. The test also took into account things like dividends and fees, which are costs associated with buying and selling stocks. The results of the test from 2020 to 2024 showed a return of -7.421094% and a maximum drawdown, or loss, of 72.280462%. A holdout test, which is a test of the strategy on new data, from 2025 to September 11 showed a return of 32.770053%. The current open position has a target price of 48.365919999999996, which was set on July 24, 2026, at 13:30:00 UTC. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run using the command line interface with the following settings: the symbol TEN, a breakout strategy, a lookback period of 20 days, 1825 days of data, a stop loss of 5%, and a target profit of 15%. The test resulted in 1250 bars and 95 trades, with a return of 60.321944%, a maximum drawdown of 18.279506%, and a win rate of 36.842105%. For comparison, buying and holding the stock would have resulted in a return of 493.742255%, with an average trade duration of 0.467107%. This test was a reproduction and not a held-out validation, meaning it was not an independent test. The test also differed from another strategy that used a simple moving average and other indicators. A separate study using the vectorbt library is pending. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars. No parameters were selected from this diagnostic test, which was only for diagnostic purposes and not for approving trades.

A news scan found the headline of an audit report for R91304 TEN. The report is about an automated trading fund manager's notes. The manager uses a system that looks at the average price of a stock over 20 days and 50 days, which is called a Simple Moving Average or SMA. The manager also looks at the opening price of a stock at 9:30 and uses a stop-loss and target price based on the stock's price. The manager uses a type of analysis called vectorbt, which is a way of looking at how a trading strategy performs. The strategy has a cost of 1 basis point fee plus 5 basis points for buying or selling, and a stress test of 15 basis points. The manager tested the strategy on historical data from 2020 to 2024 and found that it had a return of -7.54972% and a drawdown of 70.1081%. The manager also tested the strategy on data from 2025 to September 11 and found that it had a return of 60.37727% and a drawdown of 14.75847%. The manager notes that the strategy is not executable at every sample and that the actual manager runs less often. The manager also notes that there are missing observations in the data and that the strategy may not be validated for deployment. The manager concludes that the strategy has a positive regime in 2025, but that the raw training data is negative and has a large drawdown, and that the missing execution observations mean that the strategy should be watched but not validated for deployment. The data used for the analysis can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.

A daily test of the automated trading fund manager R91302 TEN was conducted. The test used the same policy as 3063, which is based on a Simple Moving Average of 50 days, and a close price greater than the prior 20 days. The test also used a next open entry with a stop loss of 2 ADR and a target of 40 sessions with a maximum stop. The test allowed for gap opens and stops on the first day. The test was run on 1682 bars of data from 2020 to September 11. The Vectorbt long only strategy was used with a fee of 1 and a slip of 5 basis points per side. The test was trained on data from 2020 to 2024 and resulted in a return of 1.30262% with a drawdown of 70.98575% and 10 wins out of 33, or 30.303%. The test was then run on holdout data from 2025 to September 11 and resulted in a return of 78.94124% with a drawdown of 16.14993% and 7 wins out of 12, or 58.3333%. A fixed path slip of 15 basis points resulted in a return of 74.69777%. The test was closed on September 11 according to the window convention, but this was not a real exit. The test preserved the sensitivities of L10 and L40 without selection in observations. As of June 28, 2024, the test matched the 1131 pre-change TEN bars exactly. On July 1, 2024, the issuer ticker change was verified, and it was confirmed that the new ticker was not assumed to be the old Tenneco. A different IEX rule was used, which resulted in 1437 bars and 103 trades with a return of 58.0091% and a drawdown of 19.158%, and 37.8641% wins. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.globenewswire.com/news-release/2024/07/01/2906633/0/en/TEN-Ltd-NYSE-Ticker-Change-to-TEN-Effective-Today.html. The current price is 47.9, which is greater than 44.71. The training drawdown was large, and the mean confidence interval crossed zero, which requires a raw 15-minute audit and a cash distribution policy before live use. The adjusted bar returns do not show the actual fund cash credit. No order was placed.

A news scan found the headline 'R91302 KMT frozen L20 training rejects a scarcity breakout trade'. This refers to a trading strategy that uses a type of technical analysis called a scarcity breakout. The strategy was tested on historical stock data from January 2, 2020, to September 11, 2026, which is a total of 1682 daily bars. The strategy, called L20, looks for a specific pattern where the closing price is higher than the previous 20 closing prices and also higher than the 50-day simple moving average, which is a calculation of the average price over the last 50 days. The strategy enters a trade at the next open and sets a stop loss at 2 times the average daily range of the last 20 days and a target at 6 times the average daily range, which is a measure of the stock's volatility. The strategy also has a maximum of 40 sessions, which is a type of time limit. The strategy was backtested using a tool called Vectorbt, which is a type of backtesting software, with fees of 1 basis point and slippage of 5 basis points per side, which are types of trading costs. The results showed a return of -48.9617% and a drawdown of 54.1759% during the training period from 2020 to 2024, with 7 out of 27 winning trades, which is a 25.9259% win rate. During the holdout period from 2025 to September 11, 2026, the strategy had a return of 10.7920% and a drawdown of 39.6636%, with 5 out of 14 winning trades, which is a 35.7143% win rate. The strategy was also tested with a fixed slippage of 15 basis points, which resulted in a stress test return of 7.7328%. The results also showed that the strategy had negative sensitivities to changes in the parameters L10 and L40 during the training period, but not during the holdout period. A command-line interface diagnostic test of 1440 bars and 65 trades showed a return of -26.3901% and a drawdown of 34.7633%, with a win rate of 30.7692%, but this was not a validation test. The data used for this test came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars. The trading engine was initially set up to allow short selling, but this was corrected to only allow long positions, and the portfolio return was calculated as the product of the returns of each individual trade, with a precision of 1e-8. The preliminary results from the wrong engine settings were not recorded as part of the analysis. The daily barriers used in the strategy were idealized and did not take into account the actual 15-minute venue management and dividend cash, and no live authorization was given. The training used a half-Kelly criterion, which is a type of betting strategy, with a size of 0, and the current size of 29.44 is less than the optimal size of 31.81, which implies a size of 0.

A diagnostic and parameter stability test was run on the CNQ stock using a command line interface, or CLI, with the following parameters: the node was set to /app/agent/dist/backtest.js, the symbol was CNQ, the strategy was breakout, the lookback period was 20 days, the number of days was 2100, the stop percentage was 6, and the target percentage was 18. The test yielded 1439 daily bars from the IEX exchange, 106 trades, a return of 23.1053%, a drawdown of 21.2717%, and a win percentage of 39.6226%. For comparison, a buy and hold strategy would have returned 104.1599%. The CLI uses signal exits and does not account for fees or slippage, which is a measure of the difference between expected and actual trade prices. This test is not the same as the frozen 3R strategy or the held out test, so it is not sufficient for making trades. An independent audit using vectorbt, a backtesting library, was also run with SIP, or securities information processor, data and explicit costs. The test results showed sensitivity to different parameters: training base returns were 5.375%, 15.6616%, and 4.6417% for different parameter sets, while holdout returns were 20.9743%, 15.3639%, and -7.8131%. Stress tests, which simulate difficult market conditions, showed returns of -6.8202%, 8.9260%, and -0.8591% for training and 17.7518%, 12.7409%, and -9.7288% for holdout. The results indicate that the parameters are fragile, meaning small changes can greatly affect performance. The L20 parameters should be kept frozen, and the better holdout results for L10 should not be chosen. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the scripts and cached bars are available in the research folder of this workspace, with persistence via recorded recipe and manifest.

A preregistered trading strategy, called 2804, was tested before results were known. The strategy used a prior 20-day close breakout and a 50-day Simple Moving Average, which is a calculation of the average price of a stock over a certain period, to decide when to enter a trade. The next day's opening price was used to enter the trade, with a baseline entry of 5 basis points, which is a unit of measurement for interest rates and investment returns, and a stop loss of 2 Average Daily Ranges, which is a measure of how much a stock's price moves on average, and a target of 6 Average Daily Ranges. The trade was limited to a maximum of 40 sessions. The test was run from January 1, 2025, to September 11, 2026. The test used a dividend-adjusted proxy, which takes into account the impact of dividends on the stock price, rather than raw cash entitlement. The test also used a Vectorbt long-only cash portfolio, which is a type of investment portfolio that only holds cash and long positions, with fees of 1 basis point and slippage of 5 basis points on each side of the trade. The test included open and close marks, entry day exits, and conservative stop losses on the first day of ambiguity, and gap open exits. A 15 basis point stress fix was applied to the baseline barriers and the same entry and exit dates were used. An earlier diagnostic test, 2808, was superseded. The report includes returns on marked open positions where present, and the win rate is calculated on closed trades only. A manual reconciliation error of less than 4e-16 was found. The test did not include validation of 2R quarters or 15-minute/off-hours trades, and did not claim to execute corporate actions. The main result, L20, was positive but based on a small sample. Sensitivity tests for L10 and L40 were not selected. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNQ&timeframe=1Day&feed=sip&adjustment=all. There is no order, and the current price of 50.07 is less than the prior 20-day close of 51.33.

A preregistered strategy, number 2804, was tested before results were known. The strategy used a prior 20-day close breakout and a 50-day Simple Moving Average, which is a calculation of the average price of a stock over 50 days. The next open was used as the entry point, with a baseline entry of 5 basis points, which is 0.05%, and an entry stop loss of 2 Average Daily Ranges, which is a measure of the average daily price movement, and a target of 6 Average Daily Ranges. The maximum number of sessions was 40. The test was run from January 1, 2021 to December 31, 2024. The test included a warm-up period in 2020 and used dividend-adjusted prices, which means the prices were adjusted to reflect the payment of dividends. The test also included a 1 basis point fee and 5 basis points of slippage, which is the difference between the expected price and the actual price, on each side of the trade. The test used open and close marks, entry day exits, and a conservative stop loss on the first day of ambiguity, and exited on gap opens. A 15 basis point stress fix was applied to the baseline barriers and the same entry and exit dates were used. An earlier diagnostic test, number 2808, was superseded. The report return includes the marked open position where present, and the win rate is based on closed trades only. A manual reconciliation error of less than 4e-16, which is a very small number, was found. The test did not include validation of 2R quarters or 15-minute or off-hours trades, and did not claim to execute corporate actions. The main test, L20, was positive but the sample size was small. The test did not select L10 or L40 sensitivity. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CNQ&timeframe=1Day&feed=sip&adjustment=all. There is no order, and the current price is 50.07, which is less than the prior 20-day price of 51.33.

The automated trading fund manager reran a required command line interface, which is a way to interact with a computer program using text commands. The command was: node /app/agent/dist/backtest.js --symbols FRO --strategy breakout --lookback20 --days2080 --stop-pct10 --target-pct30. This means the program was testing a strategy on the stock symbol FRO, using a breakout method, looking back 20 days, over 2080 days, with a 10 percent stop loss and a 30 percent target gain. The result was 1426 daily bars of data from the IEX exchange and 117 trades. The command line interface includes rules for when to exit a trade, percentage barriers, but does not account for holding onto a trade independently or the cost of making trades. This is different from another policy, called L20/SMA50/2ADR/40-session SIP, which had a negative result. This negative result cannot be replaced with a positive result from another test or hidden. No trades were made based on this command line interface test. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars.

A fund manager made notes about a trading strategy called Frozen specification note2629, which was clarified in note2631. The strategy checks if the daily closing price is higher than the average of the last 50 days and the previous 20 days. If it is, the strategy buys the stock at the next scheduled opening time on the New York Stock Exchange at 9:30. The strategy uses a stop-loss, which is a price at which to sell the stock to limit losses, set at 2 times the average daily range of the last 20 days. The target price is 3 times the range. The strategy will sell the stock after a maximum of 40 sessions. If the stock price reaches 2 times the range, the strategy will sell a quarter of the stock and move the stop-loss to the break-even point. The strategy will sell the rest of the stock at the observed price plus any additional costs. The strategy does not consider dividend payments, only the stock price. There have been 16 closed trades and 1 open trade, with a win rate that does not include the open trade. The strategy was trained on historical data, including one trade with missing data from March 8, 2022. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars. The results of the strategy will be used to decide when to execute the next trade, but do not predict future performance, including a forthcoming 6.93% distribution.

A news scan found the text of a specification note for an automated trading fund manager. The note explains that the fund will enter a trade when the daily close exceeds the prior 20 closes and the 50-day simple moving average, which is a calculation of the average price of a stock over 50 days. The fund will enter the trade at the next scheduled New York Stock Exchange opening at 9:30, with a stop loss at 2 times the prior average daily range, and a target at 3 times the risk. The fund will also use a breakeven stop, which means that if the trade is profitable, the stop loss will be moved to the entry price, and the trade will be closed at a quarter of the target, which is 2 times the risk. The fund will exit the trade at the observed sampled price plus slippage, which is the difference between the expected price and the actual price, and never at the extreme price within a bar, which is a unit of time. The fund will also reconcile manual cash orders within a small margin of error, and will account for fees of 1 basis point per side, and slippage of 5 basis points and 15 basis points. The fund will handle early closes and will not account for dividend credits. The training data contains 31 closed trades and 0 open trades, with a win rate that excludes the open trade. The data also contains one trade with missing entry-day samples from March 8, 2022, but the envelope rules, which are rules that determine the range of prices, do not indicate a missed exit or partial trigger. The historical dates have already been viewed for other rules, and this is a chronological holdout for the frozen implementation, not globally unseen evidence. The costs can alter the barriers and dates, which explains the non-monotone variant behavior. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. The positive results will advance the execution watch, but are not a forecast for the forthcoming 6.93% distribution.

A news scan found the specification note for R91208 FRO, which includes clarification notes 2629 and 2631. The raw SIP daily close exceeds the prior 20 closes and the 50-day simple moving average, which is a calculation of the average price over the last 50 days, also known as SMA50. When this happens, the system enters a trade at the next scheduled New York Stock Exchange opening at 9:30, which is a specific time when the market opens, with a stop loss set at 2 times the prior average daily range, which is the average difference between the high and low prices of the last 20 days, and a target of 3 times the range, which is the difference between the high and low prices. The maximum number of sessions for the trade is 40. The system uses actual slipped entry, which means it takes into account any delays in entering the trade, and samples subsequent 15-minute opening prices during regular trading hours, which is the time when the market is open. If the stop loss is triggered before the target, there will be no partial management of the trade. The trade exits at the observed sampled price plus slippage, which is the difference between the expected and actual prices, but never at the extreme price within a bar, which is a unit of time. The final exit is at the scheduled close minus 15 minutes on session 40. The system uses full-equity fractional long-only orders, which means it only buys and never sells short, and reconciles manual cash within a very small margin of 1e-9. The fee is 1 basis point per side, which is a unit of measurement for interest rates, and the slippage is 5 basis points and 15 basis points for stress testing. The calendar handles early closes, which means it takes into account any days when the market closes early. The system uses zero dividend credit and static raw barriers, which means it only looks at the price of the stock and not the total return including dividends. The account has 16 closed trades and 1 open trade, and the win rate excludes the open trade. The training data contains one trade with missing entry-day samples from March 8, 2022, but the envelope rules, which are used to determine the stop loss and target, did not trigger a missed exit or partial trigger. The historical dates have already been viewed for other rules, and this is a chronological holdout for the frozen implementation, not globally unseen evidence. Costs can alter barriers and dates, which explains non-monotone variant behavior. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. Positive results will advance the execution watch, but this is not a forecast for the forthcoming 6.93% distribution.

A fund manager's note explains the rules for an automated trading system. The system checks if the daily closing price is higher than the previous 20 closes and the 50-day simple moving average, which is a calculation of the average price over 50 days. If it is, the system enters a trade at the next scheduled opening time on the New York Stock Exchange, which is 9:30. The system uses a stop-loss order, which is an order to sell a stock when it falls to a certain price, set at 2 times the average daily range of the previous 20 days. The target price is 3 times the range, and the trade can last up to 40 sessions. The system also considers slippage, which is the difference between the expected price and the actual price of a trade. If the stop-loss is triggered before the target price, the system does not use partial management, which means it does not sell part of the trade. The system exits the trade at the observed price plus slippage, but never at the highest or lowest price of the session. The system uses a type of order called vectorbt, which is a way of placing trades, and reconciles the trades with the cash in the account. The system also considers fees, which are costs of trading, and slippage. The system does not consider dividend credits, which are payments made to shareholders, and only looks at the price of the stock. The training data includes 31 closed trades and 0 open trades, and the win rate does not include the open trade. One trade is missing entry-day samples, but the system's rules mean that the missed exit and partial triggers are not possible. The system does not have any missing scheduled bars. The historical dates have already been used for other rules, and this is a test of the frozen implementation, not a test of the system on new data. Costs can affect the system's performance and alter the dates of trades, which can explain why the results are not always consistent. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars. The positive results mean that the system will be watched more closely, but they do not predict future performance, including the forthcoming 6.93% distribution.

A command was given to run a test on two stocks, FRO and STNG, using a strategy called breakout. This strategy looks at the prices of the stocks over the past 20 days and decides what to do based on that. The test ran for 2080 days, with a stop loss of 10 percent and a target gain of 30 percent. The test used a method that makes decisions based on signals and also considers percentage stops and targets. It did not account for the costs of actually buying or selling the stocks and did not set aside any data to check the results independently. This test was a diagnostic tool, not a way to exactly copy a specific investment policy. The results of this test and another tool were kept, even though they were different, because the methods used were different. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars.