Stats

A trading strategy was tested on the stock FTK. The strategy had three main rules: (1) buy when the previous closing price was higher than the highest closing price of the last 10 days and the 50-day moving average, which is a calculation of the average price over 50 days; (2) buy at the next opening price, with a stop-loss at 2 times the average daily range of the last 20 days, and a target price at 6 times the average daily range, and sell after 40 trading sessions; (3) consider 1 basis point fees and 5 basis points slippage, which is the difference between expected and actual trade price, on each trade, with data from 2020 used for warm-up, 2021-2024 for training, and 2025 to September 11, 2026, for testing. The test used a type of investment strategy called a long-only full-cash portfolio, which means only buying stocks and holding cash, and used two events per day to allow for same-day exits. The test results showed 15 closed trades and 1 unfinished trade, with 1 trade closed on the same day it was opened. The conclusion was to reject the strategy due to a negative geometric return, which is a measure of return that takes into account the compounding of returns. For comparison, a buy-and-hold strategy would have returned 185.580%. This test was not meant to predict specific events and did not try to find the best parameters for the strategy using the test data. The stock FTK was chosen after the fact, and changes in the company or market conditions may affect the results.

The automated trading fund manager made the following premises: the previous closing price was higher than the previous 10-day high and the 50-day moving average, which is a calculation of the average price over 50 days. The manager entered the market at the next open, with a stop entry at 2 times the average daily range of the previous 20 days, and a target at 6 times the average daily range. The manager exited after 40 sessions. The manager also considered 1 basis point fees and 15 basis points slippage on each side, where basis points are units of measurement for interest rates or fees. The manager used data from 2020 as a warm-up, trained from 2021 to 2024, and used a separate holdout period from 2025 to September 11, 2026. The manager found that the primary L20, which is a type of machine learning model, could not be replaced by a 10/40 sensitivity analysis. The manager used a source of stock bar data from a website. The manager used a long-only full-cash portfolio, which means the manager only bought stocks and did not short sell, and used two ordered events per day to retain entry-day exits and gap exits at open. The manager found that the net returns included entry fees in the denominator, and the portfolio's compounded return reconciliation passed. The manager made 24 closed trades, and excluded 1 unfinished path and 2 entry-day exits from the analysis. The manager concluded that the strategy should be rejected due to a negative geometric return, which is a measure of the return of an investment. The manager compared this to a buy-and-hold strategy, which had a return of -25.779%. The manager noted that this was not an event-specific backtest, and did not select parameters from the holdout period. The manager also noted that the stock FTK was selected retrospectively, and that corporate and regime changes may weaken the transferability of the results.

The automated trading fund manager made the following premises: the fund will enter a trade if the previous day's close is higher than the previous 10-day high 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 day's opening price, with a stop loss at 2 times the average daily range of the previous 20 days, and a target at 6 times the average daily range. The trade will be exited after 40 sessions. The fund will also account for 1 basis point fees and 5 basis point slippage on each side of the trade, where a basis point is a unit of measurement equal to 0.01%. The data used for training the model is from 2020, and the model was trained from 2021 to 2024, with a separate holdout period from 2025 to September 11, 2026. The source of the data is a website that provides stock market data. The model uses a long-only full-cash portfolio, which means it only buys stocks and does not short sell, and it uses two ordered events per day to retain entry-day exits and gap exits at the open. The model also uses a system to handle ambiguous high and low stops. The results of the model show 24 closed trades, with 1 unfinished trade excluded from the results, and 2 entry-day exits. The conclusion is to reject the model due to a negative geometric return, which is a measure of the return on investment. The buy and hold return for comparison is -25.779%. This is not an event-specific backtest, and no parameter selection was made from the holdout period. The stock FTK was selected retrospectively, and corporate and regime changes may weaken the transferability of the results.

A trading strategy called R91200 used a breakout method, where a stock's price had to go above a certain level, called the breakout20, within 20 days, with a stop loss of 8 and a target price of 24. This strategy was tested on a set of stock data from IEX, which included 1373 bars of data and 125 trades. The results showed a return of -4.39663%, a maximum drawdown of 22.97377%, and a win rate of 33.6%. For comparison, a buy and hold strategy would have given a return of 626.78063%. The same strategy was also tested with a different set of rules, where the trade was closed based on the same signal that opened it, and there were no costs or holding periods. This was just a diagnostic test and not a real trading policy. A custom vector backtest was also run with different parameters, including 10, 20, and 40, and the results were 41.6184% and 30.6466% for the 10 parameter, and -33.4950% and -9.3330% for the 40 parameter. All 12 tests were recorded and the results were between 25254 and 65. The strategy did not switch to the 10 parameter based on the holdout test, and the 40 parameter failed due to limits on the robustness of the parameters. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars and the results were saved locally in a file called cli-hwm.json using a validation script called validate.py. No new rules or orders were added.

A protocol note, number 2455, was frozen before it was used to analyze past data. This note said to close a position when it was higher than the close price of the prior 20 days and higher than the simple moving average of the last 50 days, then enter a new position at the next open. The stop loss was set at 2 times the average daily range of the last 20 days, and the target was set at 6 times the average daily range. If there was a gap at the open, the position would be closed. The stop loss would also be triggered if it was touched first, and the position would be closed after 40 sessions. This strategy was only for long positions, meaning it only bought stocks, and it used cash. The warm-up period was in 2020, the training period was from 2021 to 2024, and the holdout period was from 2025 to September 11, 2026. The boundaries started flat. Vectorbt orders used separate slots for opening and intraday trading, and they were set to only allow long positions, to avoid unintended short positions. The returns, including fees, matched the manual calculations. There were no open terminal marks. The fees were 1 basis point per side, and the slip was 5 basis points per side, with a stress test of 15 basis points. The return was 18.4662%, the drawdown was 23.4817%, and the win rate was 35.7143%. The stress test result was 15.1951%. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=HWM&timeframe=1Day&feed=sip&adjustment=all. The daily touch fills and adjusted prices did not match the execution that would happen in a real venue, with 15-minute or overnight execution, or with 2R management or cash dividend entitlement. There were no costs from live fills. The historical holdout was in chronological order, but it was not a pristine prospective validation. The current entry was false, and there were no orders. However, the positive base and stress results suggest that the candidate should be preserved. There were 40 failed sensitivity tests, and the mean confidence interval cross and current competition regime remain unresolved. The artifacts can be found in validate.py, backtests.json, and trades.json, with observations from 25254 to 25257.

A news scan found the protocol note 2455 for the R91200 HWM frozen 3R daily proxy train, which is positive but not execution approval. The protocol note has the following details: it was frozen before history, completed close is prior 20 close high and simple moving average 50, enter next open, fixed 2 ADR 20 stop and 6 ADR target, gap exits at open, stop-first dual touch, 40 session exit, and long only cash. The warm-up period was 2020, training was from 2021 to 2024, and holdout was from 2025 to September 11, 2026, with boundaries starting flat. Vectorbt orders use separate opening and intraday slots, and explicit long only fixed unintended default shorts before accepting results. Every closed cash return, including entry fees, reconciles manual calculation, with no open terminal mark, fees of 1 basis point and slip of 5 basis points per side, and stress of 15 basis points slip. The return is 62.5755 percent, drawdown is 23.2279 percent, and win is 46.6667 percent, with stress of 53.1078 percent. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=HWM&timeframe=1Day&feed=sip&adjustment=all. Daily touch fills and adjusted prices do not reproduce venue 15 minute or overnight execution, plus 2R management or cash-dividend entitlement, with no costs observed from live fills. Historical selected-name holdout is chronological, not pristine prospective validation, with current entry false and zero order. A strong counterargument is that the positive base and stress warrants preserving the candidate, with sensitivity of 40 fails, mean CI cross 0, and current competition regime remaining unresolved, and artifacts can be found in validate.py, backtests.json, and trades.json, with observations from 25254 to 25257.

A diagnostic test was run on the CLI node using the backtest.js script with the following parameters: the symbol TSM, the breakout strategy, a lookback period of 20, 2000 days, a stop percentage of 8, and a target percentage of 24. The test inspected the source IEX and used the own signal exit with no holdout, resulting in 1372 bars, 103 trades, a return of -35.83409%, a drawdown of 36.69201%, and a win percentage of 29.12621%. The buy and hold return was 265.52872%. This test does not validate the SIP3R policy. The vectorbt costs and date boundaries were corrected for observations 25100-25111, and all stress training results were negative, as were all base holdouts. The L10 and L40 results are pre-declared sensitivity and do not provide new independent confirmation or permission to pick the least negative option. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TSM&feed=iex, with SIP version 2709. No order was placed.

A trading rule for TSM, a high-revenue stock, was tested. The rule had conditions: the stock's last 20 days' close price was higher than the prior 20 days' highest close price and higher than the 50-day simple moving average, with an entry at the next open, a stop loss at 2 times the average daily range, and a target at 6 times the average daily range, with a 20-session time exit. If there was a gap at the open, the stop loss was triggered first if it was touched twice. The rule was trained on data from 2020 to 2024 and tested on data from 2025 to September 10, 2026, with fees of 5 and a slippage of 20 basis points on each side. The training resulted in a loss of 5.5033%, while the completed trades resulted in a loss of 1.2228%. The holdout, which is a set of data not used in training, resulted in a loss of 22.185%, with 35% of trades winning and a maximum drawdown of 37.1436%. The stress test, which simulates difficult market conditions, resulted in a loss of 37.5523%. All trades in the holdout have been exited. The mean test result was a loss of 0.95275%, with a confidence interval of -4.21653% to 2.52726%, based on 20,000 bootstrap simulations, indicating no stable edge. Therefore, the rule was rejected for trading. The failure of the rule does not necessarily mean that TSM has poor fundamentals. The daily adjusted prices used in the test are not the same as cash dividend, 15-minute, or overnight venue execution prices. The rule was corrected to include same-day entry and exit, which was not possible in the earlier version. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TSM&timeframe=1Day&feed=sip&adjustment=all, and the full rows of observations are 25100-25111, with results in localstudy.py/paths/results.

A diagnostic test was run using the CLI command: node /app/agent/dist/backtest.js with the following parameters: --symbols LPG, --strategy breakout, --lookback 20, --days 2000, --stop-pct 8, and --target-pct 24. The test used 1372 IEX daily bars and resulted in 102 trades, with a return of 74.13260%, a drawdown of 13.36461%, and a win percentage of 38.23529%. The buy and hold return was 306.80883%. The test inspected the CLI source, which used IEX data, own signal exits, and no independent chronological split, and did not follow the SIP 3R policy or sizing evidence. A parameter sweep was also run with a 15-minute interval and 2R parameter, with fees of 5 and slippage of 20bp, using the same dates. The results were: L10 train 65.78465% with 34 trades, marked test 33.48742% with 16 trades, and drawdowns of 45.67749% and 29.14655%; L40 train 30.23090% with 25 trades, marked test 28.31975% with 11 trades, and drawdowns of 38.38739% and 19.09668%. Each marked test included a 1-cutoff position. The L20 remained frozen and the 10-day option was not selected after observing higher training results. All measured figures were preserved from observations 24738 to 59. The data sources used were https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the same endpoint with feed=sip. The conclusion is that the diagnostic test is complete, but wider sensitivity does not remove execution, data-boundary, and small-sample uncertainty.

A news scan found the headline 'R91106 raw-price 15-minute LPG audit: completed holdout +21.74%, stress training negative'. This is a report about an automated trading fund manager's notes. The manager uses a method called SMA50, which means Simple Moving Average over 50 days, to make decisions. The fund had a gain of 21.74% in a test period. The manager also used a stress test, which is a way to see how the fund would do in difficult conditions, and the result was negative. The fund's performance was measured over 15-minute periods, and the manager used a technique called vectorbt event replay to test the fund's decisions. The test period from 2021 to 2024 had a gain of 10.25%, and the test period from 2025 onwards had a gain of 12%. The manager also reported that the fund's performance was sensitive to changes in the market, and that the fund's rules for buying and selling stocks were not validated in real-time. The manager concluded that the current entry point for the fund is not valid, and that the fund should be watched but not invested in.

The R91106 trading strategy was tested with daily replication. The strategy is based on several premises: the stock's prior 20-day close and high, and its 50-day Simple Moving Average (SMA), which is a calculation of the average price of a stock over a certain period of time. The strategy also considers the next day's open price, the prior 20-day Average Daily Range (ADR), which is a measure of the average daily price range of a stock, and a target return of 3R, with a maximum of 20 sessions. The strategy stops trading at the first daily Open-High-Low-Close (OHLC) with gaps. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=1Day&feed=sip&adjustment=all. The strategy was trained on data from 2021 to 2024 and tested on data from 2025 to September 10, 2026. The training process used vectorbt, a backtesting library, with explicit long-only event replay, a fee of 5 basis points, and a slippage of 20 basis points on each side, and stress slippage of 50 basis points. The results were compared to manual compounding. The prior cold start, which started with 2021 data and needed a 50-bar warm-up period, resulted in a return of 48.98218086% with 28 trades. When the warm-up period was started in September 2020, the strategy had losses of -7.11%, -8.91%, and -9.62% on January 5, February 16, and March 15, respectively, but overall resulted in a return of 13.93883013% with 31 trades, a drawdown of 46.84%, and stress of -5.39983%. The same holdout period resulted in a return of 47.44972564%, a drawdown of 23.09653555%, and 15 trades, including one forced cutoff mark, and stress of 34.75874485%. The conclusion is that the earlier output was not arithmetically false, but the training depends strongly on the data boundary, and full-calendar training weakens the sizing. The validity of the results is explained by the materially changed sample, not improved alpha, which refers to the excess return of a stock or portfolio over a benchmark. The soundness of the results is already inspected, and the symbol and history are chronological validation, not untouched selection. It is recommended not to choose a cold start after seeing losses. The last trade on August 18, which was 17 sessions by September 10, is marked but not completed, and is excluded from subsequent exit simulations. There are no orders.

A command was run using a node to test a trading strategy on the stocks CSTM and KALU. The strategy used is called a breakout strategy, which means it looks for a stock to break out of a certain price range. The test looked back at 20 days of data and ran for 1500 days. It used a stop loss of 8 percent and a target gain of 24 percent. The data for the test came from a website called https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex. This test used its own signals to decide when to buy or sell, and it looked at the full history of the stocks. The results of this test cannot be used to replace other tests that use frozen data. This test was not a live test and did not use a holdout sample, which is a separate set of data used to verify the results. The test was run again to save the full results, but it did not produce a new independent result. The conclusion is that the test was run as a diagnostic, but it does not provide any information about how the stocks will perform in the future.

A command was run using a node to test a trading strategy on the stocks CSTM and KALU. The strategy used was called breakout, which means it looks for when a stock price breaks out of a certain range. The test looked back 20 days and ran for 1500 days. It used a stop loss of 8 percent and a target gain of 24 percent. The data for the test came from a website called https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex, which provides stock market data. The test used its own signals to decide when to buy or sell, and it used the full history of the stocks. This means the results cannot be used to replace other tests that use frozen data. The test was not a real-world test, but rather a diagnostic test to check the system. The test was run again to save the full results, but it was not a new independent test. The conclusion is that the diagnostic test was completed, but it does not provide any information about how the trading strategy would work in real life.

A preregistered note, number 2178, outlines a trading strategy for KALU, which involves closing above the prior 20-day close high and SMA50, with a next session open entry, stop at 2 times the prior 20-day average daily range, and a target of 6 times the average daily range, with a 3R ratio, and exiting on the 20th session close. The strategy was trained on data from 2020 to 2024 and tested on a holdout period from 2025 to September 10, 2026. The data source is from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The results show a return of -14.05% during the training period, with a drawdown of 55.15%, and a return of 47.76% during the holdout period, with a drawdown of 28.95%. The stress test return was 42.82%. A four-trade circular block bootstrap test with 10,000 samples showed that the mean confidence interval crosses zero. The conclusion is that the positive holdout results alone cannot fix the negative compounded training results or support retrospective sizing, and therefore, it is a research watch only, with no current signal and no defensible new allocation.

A preregistered note, number 2178, outlined a trading strategy for the CSTM stock, which is a type of aluminum-chain benchmark. The strategy was to buy the stock if its price closed above the highest price of the previous 20 days and the 50-day moving average, and then sell it when it reached a target price or after 20 days. The strategy was tested using data from 2020 to 2024, and then its performance was evaluated using data from 2025 to September 10, 2026. The results showed that the strategy had a return of -38.61% during the training period and 31.78% during the holdout period. The holdout period is a time when the strategy is tested on new data to see how well it performs. The stress test return was 27.89%. The results also showed that the strategy had a high drawdown, which is the largest decline in value from a peak to a trough, of 47.12% during the training period and 19.56% during the holdout period. A drawdown is a measure of risk. The strategy was also tested using a bootstrap method, which is a statistical technique, and the results showed that the mean confidence interval crossed zero. The conclusion is that the strategy is only for research purposes and does not currently have a signal to buy or sell the stock, and the positive results during the holdout period are not enough to make up for the negative results during the training period.

A required command was given to a node to run a backtest using the file backtest.js with the symbols VIST, SWKS, CCJ, and LEU, and a breakout strategy. The test looked back 20 periods and ran for 1500 days, with a stop loss of 10 percent and a target gain of 30 percent. The data came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The command line interface used the IEX data source without any adjustments, and it used 1030 bars of data. The signal exits were different from a custom fixed 20 session test. This test was not held out, not adopted, and not interchangeable with SIP chronological tests from 2017 to 2022. The costs were recorded for a 15 minute exit, but did not include venue mapping. The numbers recorded were for diagnostic purposes only, and no order was given.

A required command was given to a node at the address /app/agent/dist/backtest.js to run a test on the stocks VIST, SWKS, CCJ, and LEU using a breakout strategy. This test looked back 20 periods and ran for 1500 days, with a stop loss of 10 percent and a target gain of 30 percent. The data for this test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test used the IEX data source without any explicit adjustments, and it used 1030 bars of data. The signal exits from this test were different from a custom fixed 20 session test. This test was not held out, not adopted, and not interchangeable with SIP chronological tests from 2017 to 2022. The costs were recorded for a 15 minute exit, but this was not a test of venue mapping. The numbers from this test were recorded for diagnostic purposes only, and no orders were placed.

A required command was given to a node to run a backtest using the file backtest.js with the symbols VIST, SWKS, CCJ, and LEU, and a breakout strategy. The command included parameters such as a 20-day lookback period, 1500 days of data, a 10% stop loss, and a 30% target profit. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The command line interface, or CLI, used the IEX data source without any explicit adjustments, resulting in 1030 bars of data. The signal exits differed from a custom fixed 20-session 3R strategy. This test was not held out, not adopted, and not interchangeable with SIP chronological tests from 2017 to 2022. The costs were recorded with a 15-minute exit, but did not include venue mapping. The numbers recorded were for diagnostic purposes only and did not result in any orders.

A required command was given to a node to run a backtest on the stocks VIST, SWKS, CCJ, and LEU using a breakout strategy. This strategy, which is a type of trading approach, looks at the prices of these stocks over the past 20 periods and 1500 days. The command also included rules to stop trading if the stock price falls by 10 percent and to take profits if it rises by 30 percent. The data for this test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test used a service called IEX without making any adjustments, and it looked at 1030 periods of stock prices. The results showed that the signals to buy or sell the stocks were different from a custom strategy that uses a fixed 20-session rule. This test was not set aside, not adopted, and cannot be used with other tests that look at data in chronological order from 2017 to 2022. The costs were calculated based on a 15-minute exit, but this does not match the actual trading costs. The numbers from this test are only for diagnostic purposes and do not represent actual trades.

A frozen note, labeled as note2125, was made about the stock LPG. The note said that if the stock's closing price is higher than its prior 20-day high close and its 50-day Simple Moving Average, or SMA, which is a calculation of the average price of a stock over a certain period, then the next open would be considered. The note also mentioned a stop-loss, which is a price level at which a trader will sell a stock to limit losses, set at 2 times the prior 20-day Average Daily Range, or ADR, and a target set at 3 times the range, with an exit planned after 20 sessions. The data used for this analysis came from the period of September 10, 2021, to 2026, with the years 2021 to 2024 used for training and 2025 onward used as a holdout, or test period, with a flat start. The data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=1Day&feed=sip&adjustment=all. The fees for this trade were set at 5 basis points, or 0.05%, with a 20 basis point slip, or additional fee, on each side, and a stress test of 50 basis points. The daily high, low, open, and close prices were used to determine the stop-loss, and the long-only vector backtest, which is a type of backtest that only considers long positions, allowed for same-day round trips. The daily close price was marked, along with entry and exit marks. The trade counts and fee-inclusive manual compounding, which is the process of reinvesting returns to generate additional returns, were asserted. The return on investment during the training period was 48.9822%, with 28 trades, and during the test period, it was 47.4497%, with a maximum drawdown, or loss, of 23.0965%, and a win rate of 53.33%, with 15 trades. The stress test return was 34.7587%. The mean 95% bootstrap confidence interval, which is a statistical measure of the reliability of the results, was between -1.996 and 8.429, and it crosses zero. The current signal is false, with a price of 54.41 and a threshold of 55.2. The sensitivity, or the measure of how much the results change in response to changes in the input, was recorded at 10 and 40, but it was not used for selection. The current universe selection and the already seen history of LPG prevent the claim of untouched out-of-sample alpha, which refers to the ability of a strategy to generate returns that are not explained by the market as a whole. The daily fixed target was not validated using 15-minute intervals and a 2 times trailing return. The returns exactly match the prior returns, but the different marking yields a maximum drawdown of 23.0965% versus the prior 23.3388%, and the prior drawdown should not be overwritten or claimed as improved risk. The Half Kelly, which is a formula used to determine the optimal fraction of a portfolio to invest in a strategy, from training is 0.09000387533670048, and there is no need to input funding needs.

A news scan found the note: Frozen note2125. This note explains a trading strategy for the stock LEU. The strategy checks if the closing price is higher than the prior 20-day closing high and the 50-day Simple Moving Average, which is a calculation of the average price over 50 days. If this condition is met, the strategy will open a position at the next open. The position will be closed after 20 sessions or when the price reaches a target, which is 3 times the Average Daily Range, or when the price stops out, which means it falls below a certain level. The strategy was trained on data from September 10, 2021, to 2026, and tested on data from 2021 to 2024, with a holdout period starting from 2025. The data used can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LEU&timeframe=1Day&feed=sip&adjustment=all. The strategy includes fees of 5 basis points and a slippage of 20 basis points on each side, and a stress test of 50 basis points. The strategy only allows long positions, meaning it only buys the stock, and does not short sell. The strategy also allows for same-day round trips, meaning it can buy and sell the stock on the same day. The strategy uses daily open, high, low, and close prices, and marks the entry and exit points. The strategy's performance is measured by its return, which is 22.4505% during training and 45.8285% during testing, with a maximum drawdown of 48.5873% and a win rate of 41.18%. The strategy's stress test return is 31.6873%. The mean return with a 95% bootstrap confidence interval is between -5.944 and 16.558, which crosses zero. The current signal for the strategy is false, meaning it is not recommending a trade, with a price of 165.87 and a threshold of 194.78. The strategy's sensitivity has been fully recorded, but it is not used for selection. The current universe selection and the history of the stock LPG prevent the strategy from claiming untouched out-of-sample alpha, which refers to returns that are not explained by the strategy's factors. The strategy does not use a daily fixed target or a 15-minute validation with a 2R trailing stop. If the training data is negative or there is no current entry, the strategy will not place an order. The Half Kelly fraction from training is 0.01656102816192606, which is a measure of the optimal fraction of the portfolio to invest, and there is no need for funding input.

A news scan found the note: Frozen note2125. This note explains a trading strategy for the stock CCJ. The strategy is to buy when the close is higher than the prior 20-day high and the 50-day Simple Moving Average, which is a calculation of the average price of a stock over 50 days. The strategy will sell when it reaches a target price, which is 3 times the risk, or after 20 sessions. The data used to train this strategy is from September 10, 2021, to 2026, and it was tested on data from 2021 to 2024, with the remaining data used to validate the results. The strategy has fees of 5 basis points, or 0.05%, and a slippage of 20 basis points, or 0.2%, on each trade. The stress test result is 50 basis points, or 0.5%. The strategy only buys stocks and does not sell short. It uses daily high, low, open, and close prices to stop losses and open new trades. The strategy allows for multiple trades in one day. The training return was 10.3548% over 31 trades, and the test return was 12.4681% with a maximum drawdown of 34.2959% over 14 trades, with a win rate of 50%. The stress test result was 3.4066%. The 95% confidence interval of the mean return is between -4.200 and 7.872, which includes zero. The current signal for the stock CCJ is false, with a price of 97.42 and a threshold of 107.36. The strategy has been fully recorded and was not selected based on its performance. The current universe selection and the history of the stock prevent the strategy from claiming to have found new, unseen returns. The daily fixed target is not validated by 15-minute data or a 2 times risk trailing stop. If the training return is negative or there is no current entry signal, no trade will be made. The optimal fraction of the portfolio to invest, known as the Half Kelly, is 0.04132733473590497, and no funding need is required as input.

A news scan found the note: Frozen note2125. This note explains a trading strategy for the stock VIST. The strategy checks if the stock's close price is higher than its prior 20-day close high and its 50-day Simple Moving Average, which is a calculation of the stock's average price over 50 days. If this condition is met, the strategy will open a position at the next open price. The strategy will exit the position after 20 sessions or when it reaches a target price that is 3 times the Average Daily Range, which is a measure of the stock's daily price movement, with a stop loss at 2 times the Average Daily Range. The strategy was trained on data from 2021 to 2024 and tested on data from 2025 onward, with a flat starting point. The data used can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=VIST&timeframe=1Day&feed=sip&adjustment=all. The strategy includes fees of 5 basis points and a slippage of 20 basis points on each side, with a stress test of 50 basis points. The strategy uses daily Open, High, Low, and Close prices to stop losses and open gaps, and it only allows long positions, meaning it only buys the stock. The strategy also allows for same-day round trips, meaning it can buy and sell the stock on the same day. The strategy's performance is measured by its return, which is -29.4389% during training and 22.7833% during testing, with a maximum drawdown of 24.7675% and a win rate of 53.85%. The strategy's mean return is 13.5700% under stress, with a 95% bootstrap confidence interval of -3.721 to 8.785, which crosses zero. The current signal for the strategy is true, with a price of 78.95 and a threshold of 77.03. The strategy's sensitivity has been fully recorded, but it is not used for selection. The current universe selection and the history of the stock prevent the strategy from claiming untouched out-of-sample alpha, which refers to the strategy's ability to generate returns that are not explained by the market. The strategy does not use a daily fixed target or 15-minute validation with a 2R trailing stop. If the training data is negative or there is no current entry signal, the strategy will not place an order. The Half Kelly criterion, which is a formula used to determine the optimal bet size, is zero, and there is no need for funding input.

A news scan found the note: the trading rule for QRVO is to buy when the close is higher than the prior 20-day high close and the 50-day simple moving average, and to sell at a target price of 3 times the risk, or after 20 sessions, or at a stop loss of 2 times the average daily range. The system was trained on data from September 10, 2021, to 2026, with a holdout period starting in 2025. The data is from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=QRVO&timeframe=1Day&feed=sip&adjustment=all. The fees are 5 basis points and the slippage is 20 basis points on each side, with a stress test of 50 basis points. The system uses daily open, high, low, and close prices, and allows for same-day round trips. The training return was -17.4599% over 22 trades, and the test return was 14.5341% over 15 trades, with a maximum drawdown of 29.1677% and a win rate of 40.00%. The stress test return was 4.6761%. The 95% bootstrap confidence interval is [-2.784, 5.670], which crosses zero. The current signal is true, with a price of 112.36 and a threshold of 105.24. The sensitivity analysis is fully recorded, but not used for selection. The current universe selection and the history of LPG prevent claims of untouched out-of-sample alpha. The daily fixed target is not validated by 15-minute or 2R trailing stops. If the training is negative or there is no current entry, no order is placed. The Half Kelly fraction from training is 0, and no funding need is input.

A news scan found the note: Frozen note2125. This note is about a trading strategy for the stock SWKS. The strategy is to buy when the close price is higher than the prior 20-day high close and the 50-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period. The strategy will sell when it reaches a target price, which is 3 times the risk, or after 20 sessions. The data used to train this strategy is from September 10, 2021, to 2026, and the strategy was tested from 2021 to 2024, with the remaining time used to evaluate its performance. The fees for this strategy are 5 basis points, which is a unit of measurement for interest rates and percentages, and 20 basis points for each trade, with a stress test of 50 basis points. The strategy only allows for buying and selling the same stock on the same day, and it uses the daily high, low, open, and close prices to determine when to stop a trade. The strategy's performance was evaluated using a method called backtesting, which means using historical data to see how the strategy would have performed in the past. The results show that the strategy had a return of -33.0777% during the training period, and 1.8628% during the testing period, with a maximum drawdown of 24.2922%, and a win rate of 41.67%. The strategy's performance was also evaluated using a method called bootstrapping, which means resampling the data to estimate the strategy's performance, and the results show that the mean return is between -4.128 and 5.858. The current signal for this strategy is true, with a price of 84.03 and a threshold of 76.54. The strategy's sensitivity was tested and recorded, but not used for selection. The current universe selection and the history of the stock prevent the strategy from claiming to have found a new source of profit. The strategy does not use a daily fixed target or a 15-minute validation, and it does not require any funding input. A headline from the website https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SWKS&timeframe=1Day&feed=sip&adjustment=all was found, but its content is not provided here.