Stats

A frozen note, number 2174, outlines the premises of a trading strategy. The strategy involves nine sector ETFs, with daily price adjustments, from January 4, 2016, to December 31, 2020. Each week, the strategy buys the sector that performed the worst over the previous five sessions, but only if that performance was negative. The purchase is made at the next available opening price, with a fraction of the available cash. The strategy uses a stop-loss order, which is a type of order that automatically sells a security when it falls to a certain price, in this case, 1 times the average true range over 20 days, and a target price, which is 3 times the average true range. If the target is reached, the strategy aims to make an additional 2 times the average true range. The strategy also includes a time exit, which means that the position is closed at the end of the week. There are fees of 1 basis point, plus a slippage of 5 basis points on each side of the trade. The strategy is long-only, meaning it only buys securities, and it shares cash among the different positions. The strategy does not use leverage, which means it does not borrow money to make trades. The mean trade confidence interval is between -0.21% and 0.28%, based on 5000 bootstrap samples. The reasons for closing trades are: gap stop, which means the price gap at the opening, 12 times, time exit, 114 times, stop-loss order, 88 times, and target price, 6 times. There were 26 partial exits and 25 same-day final exits. The current incomplete week is excluded from the analysis, and all observations and source data are archived. An audit of the opening partial before intraday stop did not change any of the results. The conclusion is that the primary strategy fails to be robust, and the controls cannot replace the primary strategy after inspecting the holdout data. The mechanics of adjusted dividends, 15-minute monitor fills, fractional to whole-share partial exits, slippage estimates, and pattern day trader rules remain unvalidated. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. This adaptation is not related to Nagel's 48 industry daily short portfolio.

A news scan found the following analysis of BFRI911: Premises include two main points. First, the analysis uses a rule where if the closing price is higher than the closing price 20 sessions before, the next open is used as an entry point, and the position is exited at the next open if the signal is flat. The analysis also uses a 5% stop loss and a 15% target, with the stop loss taking priority if both are hit, and an adverse open can trigger a gap stop, with a final forced close. Second, the analysis adjusts for SIP, or Securities Information Processor, which is a system that provides real-time market data, for all 1176 bars from January 2022 to September 10, 2026, with training from 2022 to 2024, and held out from 2025 to September 10, 2026. The analysis uses a long-only strategy with a 5 basis point fee and stated slippage on each side, which refers to the difference between the expected price and the actual price of a trade. Two synthetic bookkeeping rows per day allow for entry day stops, but these are not based on intraday data. The analysis concludes that momentum alone is not sufficient for a run-up, based on a technical diagnostic that is not a matched FDA event backtest, meaning it does not test for a specific event or condition. The analysis also notes that selection, current company regime, corporate actions, and slippage assumptions may limit the transfer of these results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=BFRI&timeframe=1Day&feed=sip&adjustment=all.

A news scan found the following analysis of BFRI911: Premises include two main points. First, a frozen momentum strategy was tested, where a position is entered when the lagged close is greater than the 20-session prior close, and exited when the next open is flat, with a 5% stop loss and 15% target, and the stop loss is prioritized if both are hit. Second, the strategy was backtested on data from January 2022 to September 10, 2026, with the training period from 2022 to 2024 and the holdout period from 2025 to September 10, 2026. The backtest used a 5 basis point fee and stated slippage on each side, with no tuning, and utilized vectorbt with a long-only strategy. Two synthetic bookkeeping rows per day were used to permit entry day stops, but these are not intraday data. The conclusion is to reject momentum alone for the run-up, as this is a technical diagnostic and not a matched FDA event backtest, with no established positive edge conditioned on the calendar. The selection, current company regime, corporate actions, and slippage assumptions limit the transferability of the results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=BFRI&timeframe=1Day&feed=sip&adjustment=all.

A trading strategy called BFRI911 with a fixed momentum of 20 was trained and tested. The rules were: if the closing price was higher than 20 sessions before, the next open would be an entry signal, and the position would be closed at the next open if the signal was flat. The stop loss was 5% and the target was 15%. If both were hit, the stop loss would be executed first. If the opening price was adverse due to a gap, the stop loss would be triggered. The position would be closed at the final close. The strategy was tested on data from January 2022 to September 10, 2026, with the training period being 2022-2024 and the held-out period being 2025-September 10, 2026. No tuning was done, and a 5 basis point fee was applied on each side, with stated slippage. Two synthetic bookkeeping rows were added per day to allow for entry day stops, but these are not intraday data. The results were manually compounded and validated against vectorbt, a trading backtester, with a fractional capital proxy, but without 15-minute or 2R management validation. The initial implementation did not include entry day exits, but this was corrected before journaling the results, and the initial numbers were discarded. The conclusion is to reject momentum alone for a run-up. This is a technical diagnostic, not a matched FDA event backtest, and no calendar-conditioned positive edge was established. The selection, current company regime, corporate actions, and slippage assumptions limit the transferability of the results. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=BFRI&timeframe=1Day&feed=sip&adjustment=all.

A trading strategy called BFRI911 was tested with a momentum approach. The rules were: if the closing price was higher than 20 sessions before, the next day's opening price would be used to enter a trade, and the trade would be exited at the next opening price if the signal was flat. The strategy also included a 5% stop loss and a 15% target, with the stop loss taking priority if both were hit. If the opening price was unfavorable due to a gap, the stop loss would be triggered. The trade would be closed at the end of the day if it had not been closed already. The strategy was tested on data from January 2022 to September 10, 2026, with the data from 2022 to 2024 used for training and the data from 2025 to September 10, 2026, held out for testing. No adjustments were made to the strategy, and it was tested with a 5 basis point fee and slippage on both sides of the trade. Two synthetic rows were added to the data each day to allow for entry day stops, but these were not based on intraday data. The results were compared to the results from a manual calculation of compounded net return and trade counts, using a proxy for fractional capital and without validation of 15-minute intervals or 2R management. The initial implementation did not include entry day exits, but this was corrected before the results were recorded, and the initial numbers were discarded. The conclusion is that momentum alone is not a good strategy for this run-up. This is a technical diagnostic test, not a matched FDA event backtest, and no positive edge was established based on the calendar. The results may not be transferable due to limitations in the selection of the company regime, corporate actions, and slippage assumptions. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=BFRI&timeframe=1Day&feed=sip&adjustment=all.

A news scan found the headline about a built-in CLI breakout diagnostic that is not executable replication. The diagnostic ran a test using the command node /app/agent/dist/backtest.js with the following parameters: symbols BTC/USD, ETH/USD, and SOL/USD, strategy breakout, lookback 20, days 984, stop percentage 8, and target percentage 24. The results were: for BTC, 985 bars, return 46.46075, drawdown 14.69243, win 34.32836, and number of trades 67; for ETH, 985 bars, return 24.55541, drawdown 24.94326, win 29.09091, and number of trades 55; for SOL, 747 bars, return 48.32999, drawdown 18.61323, win 52.63158, and number of trades 38. The source code audit showed that the engine.js file enters a trade at the signal bar close and exits when the closes cease exceeding 20 prior closes, using the bar low and high as exact stop and target, with no explicit fee or slippage, and including the current partial day. The bars.js file is single page only. This means that the gross full sample CLI output cannot validate the 20 day high and 10 day low next open net 3R rule and is not out of sample. The positive headline returns were rejected as deployment evidence. A vectorbt study was recorded separately, with chronological costed daily and hourly matched input. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the files /app/agent/dist/backtest/{engine,strategies,bars}.js. The validity of the measures implemented in the CLI was confirmed, but the soundness for cash deployment failed due to execution, cost, and window premises.

A trading strategy called C91102 SOL has been tested. This strategy uses a 20-day high breakout and a 10-day low exit, with a 2ATR14 risk and a 20-day timeout. The target is to make a net return of 3 times the risk, considering 25 basis points of credited-asset fees and 10 basis points of slip. The strategy also includes a 2ATR absolute trailing stop. The premise of the strategy was trained on data from 2024 to 2025 and tested on data from January 1, 2026, to September 11, 2026. The results showed a return of -9.58405% and a maximum drawdown of 33.33683%. Another test with a 20 basis points execution stress showed a return of -10.84106%. The strategy did not have any current daily or hourly trigger. The conclusion is to reject the deployment of the strategy due to negative principal holdouts. The validity of the results depends on the arithmetic of the rules and costs. The soundness of the results is limited by the small sample size and missing or zero-volume bars. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

The automated trading fund manager has issued a report on the C91102 ETH strategy. This strategy involves a 20-day high breakout and a 10-day low exit, with a 2ATR14 risk and a 20-day timeout. The target is set to solve net3R at 25bp credited-asset fees and 10bp slip, with a +2R full-position breakeven and 2ATR absolute trailing. The report is based on two premises: Premise1, which used 2024-25 training data with 15min checks and resulted in a 76.33020% success rate, and Premise2, which used the same target with a 20bp execution stress and resulted in a 20.71455% success rate. Premise3 used holdout data and resulted in a mean of 3.56329% and an average win/loss of 6.88094%/3.07202%. The report also includes controls, such as a 15min no-trail return of 18.74207% and 10/30day neighbors with returns of 14.16928% and -20.01429%. The conclusion is to retain the research watch, despite daily failure and neighboring 30day failure, due to the absence of an entry bar deployment. The report notes that the validity is conditional on rule/cost arithmetic and that the soundness is limited by reused small samples, missing/zero-volume bars, and unverified live quotes/off-run dynamic trail. The report is based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars.

A trading strategy called C91102 for Bitcoin, which uses a 20-day high breakout and a 10-day low exit, with a 2ATR14 risk management system and a 20-day timeout, has been evaluated. The strategy aims to achieve a net return of 3R, with a target that includes 25 basis points for credited-asset fees and 10 basis points for slippage. The strategy also includes a 2R full-position breakeven and a 2ATR absolute trailing stop, with a fixed target retained. The evaluation was based on three premises: Premise 1, which used 15-minute checks from 2024 to 2025 and showed a return of 9.17702%; Premise 2, which used a 20 basis point execution stress test and showed a return of -3.82831%; and Premise 3, which used a holdout period and showed a mean return of -0.12225%. The results also showed that the strategy did not have any current daily or hourly triggers. The controls used for comparison showed a return of -2.81737% for a 15-minute no-trail strategy, and 4.81000% and -15.25060% for 10-day and 30-day neighbor strategies, respectively. Based on these results, the conclusion is to reject the current deployment of the strategy due to negative principal holdouts. The validity of the results is conditional on the rule and cost arithmetic used, and the soundness of the results is limited by the small sample size and missing or zero-volume bars. The results are based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars.

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 news scan found the headline about SFIX being a costed event-run-up stock proxy, with an unproven option edge. An edge test was done on several stocks, including PLAY, VRA, SFIX, and TCOM, with specific option prices and dates, such as PLAY's Oct 16 C 10 at $0.40 and P 7 at $0.50, which required a 27.294982% and -20.440636% change, respectively. The test results showed that all of these failed to meet a 40% threshold, with no documented actual option expected payout greater than or equal to 3 times the initial investment. The sources of this information include issuer schedules and data from https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-bars, with catalog closes dated September 9 checked with September 10 delayed bars. The premises of this test include a daily close-marked portfolio with an initial value of $1000, full stock notional, and long or short direction with a 2, 3, or 5 session lag sweep. The results of the test for SFIX, with a lag of 5 and a long direction, showed a return of 20.80233886442895 and a drawdown of 10.53865980656219 during the training phase, and a return of 9.472963631986762 and a drawdown of 6.811989100817417 during the holdout phase. However, the two holdout trades were not enough to establish a stable expectancy, and the positive underlying proxy did not model option theta, vega, or spread. There were no actual option historical returns, and no strategy was adopted.

The automated trading fund manager tested the stock of TCOM as a proxy for an event. The test involved buying and selling options of TCOM and other stocks, including PLAY, VRA, and SFIX, to see if the strategy would work. The manager used a computer program to analyze the data and make decisions. A key concept used here is 'option edge', which refers to the potential profit from buying and selling options. The test results showed that the strategy did not work well for any of the stocks, with required price changes of +27.294982% and -20.440636% for PLAY, -21.311475% for VRA, -17.543860% for SFIX, and +17.105943% and -10.801034% for TCOM. The manager used a'vectorbt daily close-marked portfolio' which is a way of tracking the performance of a group of stocks, and'slippage' which refers to the difference between the expected and actual price of a trade. The manager also used 'lag' which means waiting for a certain number of sessions before making a trade. The results were not good enough to adopt the strategy, with no trades showing a return of more than three times the investment. The manager also noted that the strategy did not take into account the 'theta','vega', and'spread' of the options, which are measures of their price and risk. The manager decided not to use this strategy because it did not work well and did not have enough historical data to support it. A news scan found the data used in the test, which included issuer schedules and stock prices from https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-bars.

The automated trading fund manager tested the stock VRA as a proxy for an event. The test used options, which are contracts that give the buyer the right to buy or sell a stock at a certain price, to see if they could make a profit. The manager looked at the options for VRA with a strike price of $2.5 and an expiration date of October 16. The test required the stock price to move up or down by a certain percentage, specifically 21.311475%, in order to make a profit. However, the test failed to show a profit, with a median absolute percentage change of 10.350471%. The manager also tested other stocks, including PLAY, SFIX, and TCOM, but all of them failed to show a profit. The manager used data from the issuer's schedules and websites, including https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-bars, to make the tests. The manager assumed that the portfolio would start with $1000 and that the fees for buying and selling would be 10 basis points, or 0.1%, and that the slippage, or difference between the expected and actual prices, would also be 10 basis points. The manager trained the model using a sample of 10 events and then tested it on 2 holdout events, but the results were not stable and did not show a clear profit. The manager also found that the stock price could move in the opposite direction of the option price, resulting in a loss. Therefore, the manager did not adopt the strategy. A news scan found the data line: the manager did not use a technical screener strategy to select the stocks.

The automated trading fund manager tested the stock PLAY as a proxy for an event. The test involved buying and selling options for PLAY and other stocks, including VRA, SFIX, and TCOM, with specific prices and dates, such as the Oct16C10 option for PLAY at $0.40. The test required certain percentage changes in the stock prices, for example, +27.294982% for PLAY, but all the tests failed to meet the 40% threshold. The manager used data from issuer schedules and websites, including https://runway.adityaperswal.workers.dev/data/stock-bars, and made assumptions about fees and slippage. The manager trained a model on historical data, with parameters such as a 2-session lag and long direction, and evaluated its performance on two holdout events. However, the results were not stable, and the manager did not adopt the strategy due to the lack of historical returns data for actual options and the potential for option losses even with positive stock direction. A news scan found the data, which is a raw script output.

A computer command was used to test a trading strategy called momentum on stocks TBN, CNXC, INTG, NEOV, and NEOG. The test looked at 20 days of past data and used 730 days of stock prices to make decisions. It aimed to stop losses at 5% and target gains at 15%. The test used daily stock prices from IEX, a financial data source, up to September 10. This test did not validate the costs of buying or selling stocks and did not set aside a portion of the data to check the results independently. The results of this test are not a confirmed strategy. A separate test on stock TBN from days 2395 to 2402 was done to study the effects of the strategy, but it has limitations. No actual orders to buy or sell stocks were made. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the test was run locally using a file called cli.json.

A command was run to test a trading strategy using momentum on the stocks TBN, CNXC, INTG, NEOV, and NEOG. The test looked back 20 days and ran for 730 days, with a stop loss of 5% and a target gain of 15%. The test used daily stock prices from IEX up to September 10. This test did not validate the results independently or consider the costs of executing trades. The results are for diagnostic purposes only and have not been adopted as a strategy. A positive result does not confirm the strategy works, and a negative result shows that momentum alone is not enough. A separate test on TBN from 2395 to 2402 will be used for further study, but it has limitations. No trades were made. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the test was run locally using cli.json.

A command was run using a node to test a momentum strategy on stocks TBN, CNXC, INTG, NEOV, and NEOG. The test looked back 20 days over 730 days, with a 5% stop loss and a 15% target gain. The test used daily bar data from IEX, current up to September 10. The results are diagnostic and do not validate the strategy's performance or costs. The test found that using momentum alone is not effective. A separate test on TBN from 2395 to 2402 days ago provided additional information, but has limitations. No orders were placed. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the test was run locally using a file called cli.json.

A command was run to test a trading strategy using momentum. The command looked at the stocks TBN, CNXC, INTG, NEOV, and NEOG over 730 days, using a 20-day lookback period, a 5% stop loss, and a 15% target profit. The test used daily stock prices from IEX through September 10. The results are not validated and do not include costs or holdout periods, which are periods of time used to test the strategy without interfering with it. The test found that using momentum alone did not produce positive results. A separate test using the stock TBN and a holdout period from 2395 to 2402 was also run, but it has limitations. No trades were made based on these results. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the test was run locally using a file called cli.json.

A command was run to test a trading strategy using momentum on stocks TBN, CNXC, INTG, NEOV, and NEOG. The test looked back 20 days over a period of 730 days, with a stop loss of 5% and a target gain of 15%. The test used daily stock prices from IEX up to September 10. This test did not validate the strategy or consider the costs of executing trades. The results are for diagnostic purposes only and have not been adopted as a strategy. The test found that using momentum alone did not produce positive results. A separate test on TBN from 2395 to 2402 will be used for further study. No trades were made. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the test was run locally using a file called cli.json.

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.