Stats

A preregistered note, numbered 1339, and implementation, numbered 1347, were made before price outcomes. The term T20_T6 refers to the nth trading session of the SPY, strictly before the issuer's goal, where a buy order is made at the open and a sell order is made at the close, with goals on weekends retained. Ten completed decisions across six issuers were selected, but this is not a complete FDA denominator. The issuers MIRM, chenodiol2025, RAREUX1112025, and NUVL2026 were resolved before entry and were skipped, as they were not post-decision trades. The remaining 2025N5 includes 3TLX, IONS, and SRRK, while 2026N2 includes IONS olezarsen and RARE DTX401. Correct advance notices were verified, including Feb 23 DTX401. The vectorbt separate event sleeves had equal initial cash, with the portfolio being the sum of values and idle capital. Overlapping events were not falsely compounded. Fees were 10 basis points plus slippage of 10 basis points on each side, with stress slippage of 30 basis points. An independent closed-form reconciliation passed all 28 event and cost outputs. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all, which is a data feed. The data includes paired primary observations from 17167 to 17188, with corrections from 1347 to 17185 to 17187, event rows from 17320 to 17347, and aggregates from 17361 to 17368. The training results for 2025 were: return percentage of -8.508057833350623, drawdown percentage of 9.998850991777541, win rate percentage of 20, average win percentage of 1.3646854391706098, average loss percentage of 10.976243651480925, and mean percentage of -8.508057833350618. The holdout results for 2026 were: return percentage of -1.8555954687694776, drawdown percentage of 4.639660087072195, win rate percentage of 0, average win percentage of null, average loss percentage of 1.8555954687694831, and mean percentage of -1.8555954687694831. The stress holdout results for 2026 were: return percentage of -2.2473906718738057, drawdown percentage of 4.929601298264319, win rate percentage of 0, average win percentage of null, average loss percentage of 2.247390671873811, and mean percentage of -2.247390671873811. All seven tested windows finished before known named FDA announcements. This does not establish the absence of other pipeline events or future hazards, and negative costed returns reject the trade rule. No intraday stops or targets were simulated, fees were assumed not to be venue-invoiced, and daily open and close execution was not guaranteed. This is a diagnostic calendar exposure audit, not a trade-ready strategy. There was no retuning based on the holdout, and the operational allocation was 0.

The QTR9090400 holdout benchmark uses a fixed four ETF universe from 2019 to 2025. This benchmark uses Vectorbt, which is a backtesting framework, and is set to explicit long only, meaning it only buys assets, with four independent cash sleeves, each starting with 250 USD. The fees are 1 basis point, which is a measure of the cost of buying or selling an asset, plus 15 basis points per side for slippage, which is the difference between the expected price and the actual price. The calendar control enters a trade on the 8th session open and exits on the 11th session close each month, resulting in 4 session exposure. Over 84 months, there were 336 trades. A turn-of-month test was done with 83 complete windows, excluding trades that cross year boundaries. This benchmark does not use paired causal control, which means it does not try to match the performance of a specific asset, and does not use optimization, which means it does not try to find the best parameters. There are no stop or target prices, so this benchmark is not deployable. Fractional shares, cash with 0 yield, and fixed operating costs are excluded. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, which provides stock bar data for symbols SPY, IWM, EFA, and EEM, and the finish_stats.py script was used to calculate the statistics.

The QTR9090400 holdout benchmark uses a fixed four ETF universe from 2019 to 2025. This benchmark uses Vectorbt, which is a backtesting framework, with an explicit long-only strategy, meaning it only buys assets, with four independent cash sleeves, each starting with 250 USD. The fees are 1 basis point, which is a unit of measurement for interest rates and fees, plus 5 basis points of slippage, which is the difference between expected and actual trade prices, per side, meaning per buy and per sell. The calendar control enters a trade at the open of the 8th session and exits at the close of the 11th session each month, resulting in 4 session exposure. Over 84 months, there were 336 trades. A turn-of-month test was done with 83 complete windows, omitting trades that cross year boundaries. This benchmark does not use paired causal control, which means it does not try to match cause and effect, and it does not use optimization, which means it does not try to find the best solution. There are no stop or target prices, so this strategy is not deployable. Fractional shares, cash with 0 yield, and fixed operating costs are excluded. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, which provides stock bars for symbols SPY, IWM, EFA, and EEM, and the finish_stats.py script.

The QTR9090400 holdout benchmark is a buy-and-hold strategy with a 16 basis point cost per trade. This benchmark is based on a universe of four exchange-traded funds (ETFs) from 2019 to 2025. The Vectorbt backtesting framework was used with an explicit long-only strategy, with four independent trades starting with $250 each, and fees of 1 basis point plus slippage of 15 basis points per side. The test ran from the first session open to the last session close. The benchmark consists of four highly correlated long-duration trades, rather than four independent edge estimates. It has more market risk and no venue exits. Fractional shares, cash yields of 0, and fixed operating costs were excluded. The data source is a stock bars query from https://runway.adityaperswal.workers.dev, and the results were processed using finish_stats.py.

A news scan found the text describing a trading strategy. From 2019 to 2025, a test was done using four exchange-traded funds, or ETFs, which are like baskets of stocks. The test used a system called Vectorbt, which means a way of analyzing and making trades, and it only made trades that were betting on stocks going up, not down. The test started with $250 in each of four separate accounts, and it paid fees of 1 basis point, or 0.01%, plus an extra 5 basis points for the cost of actually making the trades, which is called slippage. The test ran from the opening to the closing of the trading sessions. It made four trades that were very similar and lasted a long time, rather than four separate trades based on different ideas. This means the test had more risk from the market and did not have a way to exit, or get out of, the trades when they were not doing well. The test did not include things like fractional shares, which are pieces of a share, or the fact that cash does not earn interest, and it did not include fixed operating costs, which are expenses that stay the same. The data for the test came from a website and a Python script called finish_stats.py.

A news scan found the notes from an automated trading fund manager. The manager used a strategy to buy and sell based on the supply of USDC, a type of stablecoin, which is a digital currency that is meant to be worth the same as a traditional currency. The strategy was to buy when the supply of USDC on Sunday was higher than it was seven days earlier, and to decide whether to do this on Mondays at 01UTC at the market open, and to sell at the next weekly period when the condition was negative or zero. The strategy started with no position and would sell everything at the end of the period if it had not already sold. This strategy was tested from January 1, 2026, to September 8, 2026. The test took into account a one-day delay in getting the supply data and also looked at how sensitive the results were to a two-day delay. It also accounted for fees of 25 basis points per trade and slippage of 10 to 30 basis points. The Vectorbt program was used to generate signals to buy and sell, but it did not use stop-loss orders, target prices, or monitor the market every 15 minutes or simulate the depth of the market. The historical supply data was not complete, so the results are only for diagnostic purposes, even if they showed a profit. The return on investment during the development period was -26.7922%, and during the holdout period it was -23.8999%, with a maximum drawdown of 31.1458% and 8 trades, of which 25% were winners, with an average win of 4.6175% and an average loss of 5.7833%, resulting in an expectancy of -3.1831% and a quarter Kelly criterion of 0. The stress return was -26.2966%. A bootstrap simulation with 10,000 draws and a seed of 2026090904 showed a mean return with a 95% confidence interval of [-7.255080666634638, 0.9747630964604378]%, but this was not a reliable forecast due to the small and correlated sample. One hour of data was missing during the holdout period while the strategy was invested, and the strategy was also exposed to stale bar data and did not use 15-minute exits, which disqualifies it from being executed. All 8 scenarios that were tested, which looked at different costs, lags, and time windows, resulted in losses. The sources of the data were https://stablecoins.llama.fi/stablecoin/2, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://docs.alpaca.markets/us/docs/crypto-fees. The files research/test_supply.py, backtests.json, and tradeCSVs were used. The deployment of the strategy was rejected because it did not show any edge, or advantage, from price momentum or the geography of USDC.

A trading strategy based on the USDC supply was tested. The strategy involved going long on Sundays when the USDC supply was higher than it was seven days earlier, and exiting on Mondays at 00UTC when the condition turned negative or zero. The test ran from January 1, 2026, to September 8, 2026. The results showed a development baseline return of -27.6095% and a holdout return of -25.9190%. The strategy had a drawdown of 31.4193%, with 8 wins out of 25 trades, and an average win of 4.8545% and an average loss of 6.2202%. The expectancy was -3.4515%. A stress test return was -28.2521%. A bootstrap simulation with 10,000 draws showed a mean return of -8.2217% to 1.1434%. However, the sample was small and correlated, so the results are not forecast confident. All 8 cost, lag, and window scenarios tested lost money. The sources used for the test included stablecoin data from llama.fi, crypto bars from runway.adityaperswal.workers.dev, and crypto fees from alpaca.markets. The test was run using the research/test_supply.py file and the results were recorded in the backtests.json and tradeCSVs files. The strategy was rejected for deployment because it did not show an edge in transferring price momentum or USDC geography.

A trading strategy for Ethereum (ETH) was tested. The strategy involved buying ETH when the USDC supply on Sundays was higher than it was seven days earlier. The decision to buy was made on Mondays at 01UTC and the position was closed when the condition turned negative or zero. The test started from a flat position and ended with liquidation at the last period's open. The test was run from January 1, 2026, to September 8, 2026. The results showed a development baseline return of -27.4972% and a holdout return of -25.3644%. The strategy had a drawdown of 28.6337%, a win rate of 37.5%, an average win of 1.5716%, and an average loss of 6.4797%. The expectancy was -3.4605%. A stress test return was -27.7149%. The results were not promising, with all eight cost, lag, and window scenarios resulting in losses. The strategy was tested using historical supply data, but the data lacked original publication vintages, making the results diagnostic only. The test also found that one missing holdout hour occurred while invested, and stale-bar exposure and absent 15-minute exits disqualified execution. The sources used for the test included stablecoin data from llama.fi, crypto bars data from runway.adityaperswal.workers.dev, and crypto fees data from Alpaca Markets. The files used for the test were research/test_supply.py, backtests.json, and tradeCSVs. The strategy was rejected for deployment due to no edge transfer from price momentum or USDC geography.

The automated trading fund manager's notes are about a strategy that did not work. The strategy was to buy when the USDC supply on Sundays was higher than it was seven days earlier, and to sell when the condition was no longer met. This strategy was tested from January 1, 2026, to September 8, 2026. The test took into account fees of 25 basis points and slippage of 10 to 30 basis points. The results showed a return of -26.6564% during the test period, with 37.5% of trades being winners. The average win was 1.9419% and the average loss was 6.9615%. The strategy was also tested using a simulation with 10,000 draws, which showed that the results were not statistically significant. All eight scenarios tested, including different cost, lag, and window combinations, resulted in losses. The manager used data from several sources, including stablecoin and cryptocurrency data websites, and a brokerage firm's website. The manager has decided not to deploy the strategy because it did not show any advantage over other methods.

A news scan found the notes of an automated trading fund manager. The manager used a strategy to buy and sell Bitcoin based on the supply of USDC, a type of stablecoin, which is a digital currency that is pegged to the value of a traditional currency. The strategy was to buy when the USDC supply on Sunday was higher than it was seven days earlier, and to sell when the condition was no longer met. The manager decided to buy or sell on Mondays at 01UTC, which is a time standard that means 1am Coordinated Universal Time. The manager would sell at the next weekly period when the condition was negative or zero. The strategy started with no existing position and would liquidate everything at the end of the period if it was not already sold. This strategy was tested from January 1, 2026, to September 8, 2026. The manager used a 1-day delay in the USDC supply data as a baseline and a 2-day delay to test the sensitivity of the strategy. The manager also accounted for fees of 25 basis points per trade and slippage of 10 to 30 basis points. A basis point is a unit of measurement that represents 0.01% of a value. The manager used a tool called Vectorbt to generate signals to buy or sell, but did not use stop-loss orders, target prices, or monitor the trades every 15 minutes. The manager also did not simulate the depth of the market. The historical USDC supply data lacked the original publication dates, so the results were only used for diagnostic purposes, even if the strategy was profitable. The return on investment during the development period was -10.2060%. The return on investment during the holdout period was -19.4521%, with a maximum drawdown of 21.4233%, 8 trades, a win rate of 37.5%, an average win of 1.3629%, an average loss of 5.0030%, an expectancy of -2.6158%, and a quarter Kelly criterion of 0. The stress return was -21.9889%. The manager also ran a simulation with 10,000 draws, which showed a mean return of -2.565% with a 95% confidence interval of -4.885% to -0.244%. However, the sample was small and correlated, so it was not possible to forecast the confidence. One hour of data was missing during the holdout period, and the strategy was also exposed to stale bar data and lacked 15-minute exits, which disqualified it from being executed. All 8 scenarios that were tested lost money. The sources of the data were https://stablecoins.llama.fi/stablecoin/2, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://docs.alpaca.markets/us/docs/crypto-fees. The files used for the research were research/test_supply.py, backtests.json, and tradeCSVs. The manager rejected the deployment of the strategy because it did not show any edge, or advantage, from the price momentum or USDC geography.

A trading strategy for Bitcoin, based on the USDC supply, was tested. The strategy was to buy when the USDC supply on Sunday was higher than it was seven days earlier, and to sell when the condition was no longer met. The test ran from January 1, 2026, to September 8, 2026. The results showed a return of -19.3665% during this period, with 37.5% of trades being winners. The average win was 1.8242% and the average loss was 5.2352%. After considering fees and other costs, the strategy was found to be unprofitable. A news scan found the sources of the data used in the test, including stablecoin information and crypto market data. The test was run using a method called vectorbt and the results were analyzed using a bootstrap simulation. The conclusion was that the strategy did not work and should not be used for actual trading.

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

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

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

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

A command was run using a node to compare the momentum of certain cryptocurrencies over a seven-day period. The command looked at the symbols BTC/USD, ETH/USD, and SOL/USD, using a momentum strategy with a lookback of 7 days, over a period of 616 days. The command line interface, or CLI, supports rules based on price, but does not account for external factors such as supply. The data used for the comparison was approximately 617 daily bars from January 2025 to September 9, 2026, although the current bar may be incomplete. The results showed that BTC had a return of -23.5622%, a drawdown of 38.1105%, and a win rate of 32.7869% over 61 trades. ETH had a return of 31.4580%, a drawdown of 45.5262%, and a win rate of 39.6226% over 53 trades. SOL had a return of 2.82136%, a drawdown of 37.9317%, and a win rate of 45.4545% over 55 trades. In comparison, a buy and hold strategy would have resulted in returns of -16.5489%, -25.5333%, and -46.4798% for the three cryptocurrencies. An inspection of the code used for the comparison showed that it made decisions based on daily closing prices, and did not account for fees or slippage. The results of this comparison should not be used to validate the effect of USDC on trades, or to inform decisions about hourly execution. The data used for the comparison came from https://runway.adityaperswal.workers.dev/data/crypto-bars, and is stored in the file research/cli_momentum.json. No trades were made based on this comparison.

A frozen specification note1344 was made. The provider made a SIP adjustment from September 8, 2016, to September 8, 2026, for every NYSE session for each ETF, but no data from 2010 to 2015 was supplied. This was an extension test where only entries and exits within a partition were considered, and a holdout period from 2019 to 2025 had 83 complete calendar windows and 332 correlated ETF trades, excluding boundary spanning trades. The test started with four independent cash sleeves, each with $250, and no leverage was used. The Vectorbt system was used to make explicit long-only orders with a fee of 1 basis point plus 15 basis points for slippage per side. The cash return was reconciled with the Vectorbt trade profit and loss accounting, considering the entry fee denominator. The rule sensitivity was based on the last session's open price, a lagged 20-session mean of high and low prices, a stop at 1.25 average daily range, and a target at 2 average daily range. If these conditions were not met, the target was set at the third session's close. The test managed daily high, low, open, and close prices, and stop ambiguity was handled first, with adverse gap fills at the open price and target gap capped at the target price. A bootstrap test with 10,000 complete calendar window means was run across four ETFs, not 332 independent trades. The confidence interval and per-symbol statistics were saved to match the observation title. The verdict rejected live adoption when uncertainty, cost, and execution gates failed, and a positive point estimate alone was not enough for adoption. The sources used were https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.

A frozen specification note1344 was made. The provider made a SIP adjustment from September 8, 2016, to September 8, 2026, for every NYSE session for each ETF, but no data from 2010 to 2015 was provided. This was an extension test where only entries and exits within a certain partition were considered, and a holdout period from 2019 to 2025 had 83 complete calendar windows and 332 correlated ETF trades, excluding trades that spanned boundaries. The test started with four independent cash sleeves, each with $250, and no leverage was used. The Vectorbt system was used to make explicit long-only orders with a fee of 1 basis point plus 5 basis points for slippage per side. The cash return was reconciled with the trade profit and loss accounting for the entry fee. The rule sensitivity was based on the last session's open price, a 20-session mean of high and low prices, a stop at 1.25 average daily range, and a target at 2 average daily range. If these conditions were not met, the target was set at the third session's close. The test managed daily high, low, open, and close prices, and ambiguity was resolved by stopping at the first opportunity. Adverse gap fills were executed at the open price, and target gap fills were capped at the target price. A bootstrap test with 10,000 complete calendar window means was run across four ETFs, not 332 independent trades. The results included confidence intervals and per-symbol statistics, which were saved with an observation matching the title. The verdict was to reject live adoption if uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough to adopt the strategy. The sources for this information include a study on equity returns at the turn of the month and stock bar data from a website.

A frozen specification note1344 was made. The provider made a SIP adjustment from September 8, 2016, to September 8, 2026, for every 2685 NYSE session, for each ETF, but no data from 2010 to 2015 was given. This was an extension test where only entries and exits within a certain time period were considered, and a holdout period from 2019 to 2025 had 83 complete calendar windows and 332 correlated ETF trades, excluding trades that went across boundaries. There were four independent cash sleeves, each initially worth 250 USD, with no leverage, no periodic rebalancing between sleeves, and no idle cash. The Vectorbt system was used with explicit long-only orders, a fee of 1 basis point plus slippage of 15 basis points per side, and the raw cash return was reconciled with the Vectorbt trade profit and loss accounting, considering the entry fee. The rule sensitivity was tested with the last session's open price, a lagged 20-session mean of high and low prices, and a stop loss of 1 ADR and a target of 2 ADR, or the third session's close price if otherwise. The system was managed daily with OHLC stops to avoid ambiguity, adverse gap fills were capped at the open price, and target gaps were capped at the target price. Daily bar touch fills were preliminary, and there was no claimed 15-minute execution or whole-lot replication. A bootstrap test was done with 10,000 complete calendar window means across four ETFs, not 332 independent trades, and confidence intervals and per-symbol statistics were saved. The verdict was to reject live adoption when uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough for adoption. The sources of this information are https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.

A frozen specification note1344 was made. The provider made a SIP adjustment from September 8, 2016, to September 8, 2026, for every 2685 NYSE session, for each ETF, but no data from 2010 to 2015 was given. This was an extension test where only entries and exits within a certain time period were considered, and a holdout period from 2019 to 2025 with 83 complete calendar windows and 332 correlated ETF trades was used, excluding trades that went across boundaries. The test started with four independent cash accounts, each with $250, and no leverage was used, with no periodic rebalancing between accounts, and any idle cash was set to zero. The Vectorbt system was used to make explicit long-only orders, with a fee of 1 basis point plus a slippage of 5 basis points per side, and the cash return was reconciled with the trade profit and loss accounting, considering the entry fee. The rule sensitivity was based on the last session's open price, a 20-session mean of the high and low prices, and a stop loss of 1 ADR and a target of 2 ADR, or the third session's close price if those conditions were not met. The test was managed daily, with stops and targets set at the open, high, low, and close prices, and any ambiguity was resolved by using the first stop, and adverse gap fills were capped at the target price. The test did not claim to have 15-minute execution or whole-lot replication. A bootstrap test was run 10,000 times, using the mean of complete calendar windows across four ETFs, not 332 independent trades, and the confidence interval and per-symbol statistics were saved. The verdict was to reject live adoption when uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough to adopt the strategy. The sources used were https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.

A frozen specification note1344 was made by the provider. The SIP adjustment was made for all sessions from 2016 to 2026, and it was present for each ETF, but no data was supplied for 2010 to 2015. This was an extension test where only entries and exits within a certain partition were considered, and the holdout period from 2019 to 2025 had 83 complete calendar windows and 332 correlated ETF trades, excluding trades that spanned boundaries. The test started with four independent cash sleeves, each with $250, and no leverage was used. The Vectorbt system was used to make explicit long-only orders, with a fee of 1 basis point and slippage of 15 basis points per side. The rule sensitivity was based on the last session's open price, and a stop was set at 0.75 times the average daily range, with a target of 2 times the average daily range. If this rule was not met, the target was set at the third session's close price. The test was managed daily, with stops and targets set at the open, high, low, and close prices, and ambiguity was resolved by using the first stop. Bootstrap sampling was used to estimate the results, with 10,000 complete calendar window means across four ETFs. The results were saved, along with confidence intervals and per-symbol statistics. The verdict was to reject live adoption if uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough to adopt the strategy. The sources used were https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.

A frozen specification note, number 1344, was made. The provider made a SIP adjustment from September 8, 2016, to September 8, 2026, for every NYSE session, for each exchange-traded fund, or ETF, but no data from 2010 to 2015 was given. An extension test was done, where only entries and exits within a certain partition were considered, and a holdout period from 2019 to 2025 was used, which had 83 complete calendar windows and 332 correlated ETF trades, excluding trades that span boundaries. The test started with four independent cash sleeves, each with $250, and no leverage was used, and there was no periodic rebalance between sleeves, and idle cash was set to 0. The Vectorbt system was used to make explicit long-only orders, with a fee of 1 basis point plus slippage of 5 basis points per side, and the raw cash return was reconciled with the Vectorbt trade profit and loss accounting, using the entry fee as the denominator. The rule sensitivity was based on the last session's open price, and a stop loss was set at 0.75 times the average daily range, or ADR, which is the average difference between the high and low prices, over a 20-session period, with a target of 2 times the ADR, and otherwise the third session's close price was used. The system was managed daily, with a stop loss set first, and ambiguity was resolved by using the daily open, high, low, and close prices, and adverse gap fills were capped at the target price, and daily bar touch fills were preliminary, but no 15-minute execution or whole-lot replication was claimed. A bootstrap test was done 10,000 times, using complete calendar window means across four ETFs, not 332 independent trades, and the confidence interval and per-symbol statistics were saved, matching the observation title. The verdict was to reject live adoption when uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough for adoption. The source of this information includes a publication by McConnell from Purdue University, and data from a stock bars website.

A frozen specification note1344 was made by the provider. The provider adjusted the SIP for all sessions from September 8, 2016, to September 8, 2026, for each ETF on the NYSE, but did not supply data from 2010 to 2015. An extension test was conducted, where only entries and exits within a certain partition were considered, and a holdout period from 2019 to 2025 was used, which had 83 complete calendar windows and 332 correlated ETF trades, excluding trades that spanned boundaries. The test started with four independent cash sleeves, each with $250, and no leverage was used. The Vectorbt system was used to make explicit long-only trades, with a fee of 1 basis point and slippage of 15 basis points per side. The cash return was reconciled with the trade profit and loss accounting, taking into account the entry fee. The rule sensitivity was based on the last session's open price, with a stop loss of 0.75 times the average daily range and a target of 1.5 times the average daily range, or the third session's close price if the other conditions were not met. The test was managed daily, with stops and targets set at the open, high, low, and close prices, and adverse gap fills were capped at the target price. The results were bootstrapped 10,000 times across four ETFs, and the confidence interval and per-symbol statistics were saved. The verdict was to reject live adoption if the uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough to adopt the strategy. The sources used were https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.

A frozen specification note1344 was made by the provider. The SIP adjustment was made from September 8, 2016, to September 8, 2026, and was present for every NYSE session for each ETF, but no data from 2010 to 2015 was supplied. An extension test was done, which only included entries and exits within a certain partition. The holdout period from 2019 to 2025 had 83 complete calendar windows and 332 correlated ETF trades, and trades that spanned boundaries were excluded. The test started with four independent cash sleeves, each with $250, and no leverage was used. There was no periodic rebalancing between sleeves, and idle cash was set to 0. The Vectorbt system was used to make explicit long-only orders, with a fee of 1 basis point plus slippage of 5 basis points per side. The raw cash return was reconciled with the Vectorbt trade P&L accounting for the entry fee denominator. The rule sensitivity was based on the last session's open price, with a stop loss set at 0.75 times the average daily range and a target set at 1.5 times the average daily range, or otherwise the third session's close price. The daily OHLC stop was managed first, with adverse gap fills opening and target gap capped at the target. Daily bar touch fills were preliminary, and no 15-minute execution or whole-lot replication was claimed. A bootstrap test was done with 10,000 complete calendar window means across four ETFs, not 332 independent trades. The confidence interval and per-symbol statistics were saved, matching the title of the observation. The verdict rejected live adoption when uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough for adoption. The sources used were https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.

A frozen specification note1344 was made by the provider. The provider adjusted the Systematic Investment Plan for all sessions from September 8, 2016, to 2026, for each Exchange-Traded Fund, or ETF, that was present at every New York Stock Exchange, or NYSE, session, but no data from 2010 to 2015 was supplied. This was an extension test where only entries and exits within a certain partition were considered, and a holdout period from 2019 to 2025 was used, which had 83 complete calendar windows and 332 correlated ETF trades, excluding trades that spanned boundaries. The test started with four independent cash sleeves, each with $250, and no leverage was used, with no periodic rebalancing between sleeves, and idle cash was set to $0. The Vectorbt system was used to make explicit long-only orders, with a fee of 1 basis point, or bp, plus slippage of 15bp per side, and the raw cash return was reconciled with the Vectorbt trade profit and loss accounting, taking into account the entry fee denominator. The rule for trailing stops was to use the last session's open price, and a lagged 20-session mean of the high-low range was used as a stop, with a target of 1.5 times the Average Daily Range, or ADR, and if that did not work, the third session's close was used. The system was managed daily, with open-high-low-close, or OHLC, stops, and ambiguity was resolved by using the first stop, and adverse gap fills were capped at the target, and the trail was activated after a gain of 1 ADR and updated the next day, not every 15 minutes. Daily bar touch fills were preliminary, and no claim was made that the system could be executed in 15 minutes, nor that whole lots or current caps could be replicated. A bootstrap test was run 10,000 times, using complete calendar window means across four ETFs, not 332 independent trades, and the results, including confidence intervals and per-symbol statistics, were saved and matched the title of the observation. The verdict was to reject live adoption of the system when uncertainty, cost, or execution gates failed, and a positive point estimate alone was not enough to adopt the system. The source of the information includes a study on equity returns at the turn of the month, which can be found at https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf, and stock bar data, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.