A news scan found the headline 'Full-major history audit confirms incomplete hours and daily aggregation discrepancies'. This refers to a review of historical data. The audit checked the hours from January 1, 2024, to September 5, 2026, for the Bitcoin and Ethereum markets, which are types of digital currencies. Out of the expected 23,496 hours, 23,492 hours were downloaded, with 976 complete days and 3 incomplete days. The audit also found some duplicate information, which was removed, and excluded partial data from September 6. The data was then checked to make sure there were no missing values in 14-day periods. The initial attempt to fetch the data failed due to too many requests, so the system was adjusted to make requests 55 seconds apart and to retry failed requests. The final data coverage was then checked. The audit compared the daily and hourly data and found some differences, with the largest difference being 6.2064% for Bitcoin and 7.9068% for Ethereum. The smallest differences were 3.9861% and 6.8874% for the low prices, 0.09466% and 0.09229% for the closing prices, and 96.6146% and 99.3558% for the volume. The audit also checked how the data was constructed, but could not explain the differences. The hourly data was used to train a model, which found one gap in the data. The audit concluded that the historical data is not completely reliable and does not match the cash ledger, which is a record of transactions. The reconstructed daily data is used as a reference, but is not considered the true data. The cause of the discrepancies is still unknown and more investigation is needed. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/historical-crypto-data-1.
A test was run using the command line interface node with the following settings: ETH/USD symbol, sma-cross strategy, 20-day fast simple moving average, 50-day slow simple moving average, 979 days of data, 5% stop loss percentage, and 7.5% target profit percentage. The test returned 980 bars of data, resulted in 80 trades, and had a profit of 33.1940%, with a maximum drawdown of 44.7658%, a win rate of 45%, and a buy and hold return of 6.51545%. The average trade duration was 0.49621%. The command line interface reads raw daily data and does not have pagination or iteration filters. The test did not include a holdout period or consideration of the ETH/BTC ratio. The 5% and 7.5% barriers were fixed and different from the range exits, and the test included a fee, slip, and partial management audit. The results should not be attributed to a single factor and should not be used to adopt the command line interface profit. This test should be recorded as a control only, as the actual candidate failed the daily and hourly net test. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the local files used were app/agent/dist/backtest/cli.js and bars.js.
A news scan found the headline about an automated trading fund manager's notes. The manager had a rule to buy Ethereum, or ETH, when its ratio to Bitcoin, or BTC, was higher than its 50-day average and ETH was also higher than its 50-day average. The manager would then set a stop loss and target based on the previous 14 days and wait for 7 days. If the trade was still open after 7 days, the manager would adjust the stop loss and target. The manager used a system called vectorbt to make trades and accounted for a 0.25% fee and a 0.1% slip for each trade. The manager trained the system from 2024 to 2025 and tested it from January 1, 2026, to September 5, 2026. The results showed that the system did not work well, with a daily return of -7.0258% and an hourly return of -9.64445%. The manager also found that doubling the cost of trades made the results even worse, with a daily return of -19.7361% and an hourly return of -21.9967%. The manager concluded that the system should be rejected and did not authorize any inverse or short trades. The manager noted that the system had some limitations, including not having all the historical data and not accounting for the dependence between different regimes. The data used for the test came from https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees were estimated based on https://docs.alpaca.markets/us/docs/crypto-fees.
A test of a defensive swing replication strategy for the EEMV fund failed to meet deployment requirements. The strategy is based on the original AQR defensive theory, which focuses on long-term lower-risk equity investments, not short-term swings. The test used an existing factor ETF with a specific set of rules, including a next-open entry on the first session of each month, a stop loss at 1 times the prior 20 session average true range, and a target of 1.5 times the stop loss, capped at 10 sessions. The test also included a daily gap stop at the open, and stopped the first session if both the stop and target were hit. The results of the test from 2016 to 2022 showed a return of -8.2106%, and from January 2023 to August 2026 showed a return of 0.8920%, with a session close drawdown of 8.6201%, and a win rate of 54.5455%. The 3-month bootstrap mean confidence interval was -0.29972% to 0.35449%. The cost of the strategy was 10 basis points one-way split fee and slip, not including venue costs. A 50 basis point stress return test showed a return of -29.0448%. The test excluded the September cohort uniformly, and used a complete history of 18,788 adjusted SIP bars across 7 ETFs through September 4. The conclusion of the test was to reject deployment, as the positive point estimate of the EEMV could not overcome the confidence interval crossing zero and the cost failure. The results were validated using Vectorbt and an independent cash ledger, which agreed within 1e-6. The test also recorded training 5/15 holding sensitivities and 2 basis point later controls, but did not select a winner. The market window used in the test has already been seen in other research, so it is not a pristine out-of-sample test. The prices used in the test were adjusted for corporate actions, but did not include cash dividend, raw stop, 15-minute, or profit management validation. The sources used in the test include a white paper from AQR and data from a stock bars website, as well as protocol note 248. The validity of the test follows the prerequisite of a positive independently executable edge, but this test rejects the adaptation, not the universal anomaly.
A test of the EFAV defensive swing replication strategy has failed to meet the deployment gate criteria. The strategy is based on the original AQR defensive theory, which focuses on long-horizon lower-risk equity, not short-term swings. The test used an existing factor ETF with a next-open entry on the first session of each month, a prior 20-session ATR stop at 1, a target at 1.5, and a maximum of 10 sessions. It also included a daily gap stop at the open, stopping the first session if both the stop and target were hit, with the target capped. The test was trained on data from 2016 to 2022, which showed a return of -7.4152%, and later data from January 2023 to August 2026 showed a return of -8.0622%, with a session close drawdown of 12.1907%, 44 sessions, a win rate of 45.4545%, a mean win of 1.08274%, and a mean loss of 1.23836%. The mean return was -0.18331%. The cost was 10 basis points one-way split fee/slip, not including venue costs. A 50 basis point stress return showed a loss of 35.3420%. The test excluded the right-censored September cohort uniformly, as per note 254, and used a complete history of 18,788 adjusted SIP bars across 7 ETFs through September 4. The conclusion is to reject deployment, as the positive EEMV point estimate cannot overcome the confidence interval crossing zero and cost failure. The Vectorbt and independent cash ledger agree within 1e-6. The training included 5/15 holding sensitivities and 2 basis point later controls, but no winner selection. The market window has already been seen in other research, so it is not pristine out-of-sample. Prices were adjusted for corporate actions, but not for cash dividends, raw stops, 15-minute intervals, or profit management validation. The sources for this test include a white paper from AQR and data from runway.adityaperswal.workers.dev. The validity of the test follows the prerequisite of a positive, independently executable edge, but this test rejects the adaptation, not the universal anomaly.
A news scan found the headline 'USMV defensive swing replication fails deployment gate'. This refers to a test of a trading strategy based on the AQR defensive theory, which is a long-term investment approach that aims to reduce risk. The strategy was adapted from an existing factor-based exchange-traded fund, or ETF, which is a type of investment fund that tracks a specific stock market index. The adaptation used a next-open entry on the first session of each month, with a stop-loss order set at 1 times the average true range, or ATR, of the prior 20 sessions, and a target of 1.5 times the ATR, with a maximum of 10 sessions. A daily gap stop was also used at the open, and the stop was triggered first if both the gap stop and the target were hit, with the target capped. The strategy was trained on data from 2016 to 2022 and returned 14.1636%, with 82 trades. However, when tested on data from January 2023 to August 2026, the strategy returned -4.4143%, with a maximum drawdown of 10.9436%, and a win rate of 43.1818%, with an average win of 1.12446% and an average loss of 1.02416%, resulting in a mean return of -0.09634%. A 3-month bootstrap analysis found a 95% confidence interval of -0.43093% to 0.23567%. The cost of the strategy was estimated to be 10 basis points, or 0.1%, one-way split fee and slippage, not including venue costs, and a 50 basis point stress return was estimated to be -32.7765%. The September cohort was excluded due to right-censoring, as per note 254, and the complete history consisted of 18,788 adjusted SIP bars across 7 ETFs through September 4. The conclusion is to reject the deployment of the strategy, as the positive expected excess return over the market value, or EEMV, point estimate cannot overcome the confidence interval crossing zero and the cost failure. The results were validated using Vectorbt and an independent cash ledger, which agreed within 1e-6. The training data included 5/15 holding sensitivities and 2 basis point later controls, but no winner selection was used. The market window used in the test has already been seen in other research, so it is not a pristine out-of-sample test, and the prices were adjusted for corporate actions, but not for cash dividends, raw stops, 15-minute intervals, or profit management validation. The sources used in the test include a white paper from AQR on defensive equity, a dataset of stock bars from Aditya Perswal, and protocol note 248. The validity of the test follows the prerequisite of a positive independently executable edge, but this test rejects the adaptation, not the universal anomaly.
A news scan found the headline 'SPLV defensive swing replication fails deployment gate'. This refers to a test of a defensive investment strategy based on the SPLV ETF. The strategy is based on a theory by AQR, which is a long-term investment approach that aims to reduce risk. The test used a set of rules, including a stop-loss order, which is an order to sell a security when it falls to a certain price, and a target price, which is the price at which the security is expected to rise. The test also used a daily gap stop, which is a stop-loss order that is triggered when the security's price gaps down at the open. The test was run on historical data from 2016 to 2022 and from January 2023 to August 2026. The results showed a return of 14.5780% from 2016 to 2022, but a return of -15.8303% from January 2023 to August 2026. The test also showed a maximum drawdown, which is the maximum loss from a peak to a trough, of 18.7584%. The strategy was tested using a bootstrap method, which is a statistical method that involves resampling the data, and the results showed that the strategy's return was not statistically significant. The test also took into account the cost of implementing the strategy, which was 10 basis points, or 0.1%, one way, and the stress return, which is the return of the strategy during a period of high market stress, was -40.8052%. The test excluded a cohort of data from September, as per a protocol note. The conclusion of the test was to reject the deployment of the strategy, as the positive expected return was not enough to overcome the costs and the uncertainty of the results. The test was run using a software called Vectorbt and the results were verified using an independent cash ledger. The test also recorded the sensitivities of the strategy to different parameters, but did not select a winner. The market window used in the test was not pristine, meaning that it had been seen before in other research, and the prices used in the test were adjusted for corporate actions, but not for cash dividends or other factors. The sources used in the test include a white paper by AQR and a dataset of stock bars. The validity of the test follows from the prerequisite of a positive, independently executable edge, but this test rejects the adaptation of the strategy, not the universal anomaly.
A Conditional Monte Carlo simulation, which is a method to estimate the outcome of a process, demonstrated a failure in selected-winner sizing. The simulation used 10,000 independent and identically distributed paths, with 10 trades each, and reinvested 10% of the current capital per trade. It sampled each diagnostic's 3-7 natural returns, but this was not a calendar-time or venue-stop simulation. For example, Maersk terminal returns were 5/50/95 percentiles of -2.259, 2.423, and 7.410 percent, with a loss of 20.38 percent and a drawdown of 95 percent at 3.637 percent. COHU returns were -1.410, 10.877, and 27.477 percent, with a loss of 7.65 percent and a drawdown of 95 percent at 5.466 percent. The AXT selected extreme produced a median return of 11430.9 percent and a 95th percentile return of 1180856.7 percent, which is an implausible investment forecast driven by resampling one huge winner, with no independent tail model. The simulation used plug-in quarter Kelly fractions of 0.475-1.754 across names, but authorized 0 for all, as every mean confidence interval spans zero and the execution model is incomplete. Positions were capped at 212.7475 USD, with a nominal risk of 8.5099 USD, and a fund value of 278.67 USD, but these caps cannot create an edge. Resampled long trend win sizes cannot justify 1-2 ADR swing targets. The results and every measured figure were recorded from May 25 to May 28, and the scripts and trades were archived. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/.
A news scan found a report about eight selected bottleneck diagnostics. The report states that eight current-survivor names were selected after a broad discovery, but the selection is not independent economic-alpha confirmation because it sees current winners. The report used a method called NumPy seed 90602 and 10,000 IID bootstrap resamples per ticker of natural net holdout trade returns, which is a way to test the results, and found that 95% percentile intervals all cross zero. The results for each ticker are: AEHR [-22.65, 162.84]%, COHU [-6.25, 33.82]%, AXTI [-26.87, 3755.10]%, MP [-9.28, 83.34]%, MU [-7.56, 81.92]%, Maersk [-5.77, 11.05]%, Lynas [-1.98, 52.82]%, and Fujikura [-21.51, 134.71]%. The report notes that five of the eight fixed training returns are negative, and that the results may understate uncertainty due to tiny sample size, overlapping market regimes, and selection. The report also mentions a statistical method called US 420 daily OLS on SPY, which is a way to measure the relationship between different stocks, and found the following results: beta AEHR 3.447, COHU 1.986, AXTI 2.511, MP 1.416, MU 2.568. However, the report states that these results are not causal supply alpha, which is a measure of a stock's performance. The report also notes that overseas same-date regression is nonsynchronous and local currency, and should be discarded for sizing. Additionally, the report states that two live supply wins were forced operator liquidations, and that there is no natural exit or loss distribution, and slippage is unmeasurable without contemporaneous quote. The prior remains zero eligible edge, which means that there is no advantage, and prospective synchronized evidence with positive cost-adjusted lower bound would increase trade eligibility. The report cites two sources: https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/, and query trades fund = supply IDs 3, 6.
A simulation of the HITI five-session path used a technique called bootstrapping, which involves randomly selecting data, to estimate possible outcomes. This process, also known as a Monte Carlo simulation, used 20,000 events and 10,000 draws from 165 blocks of five-session data from 2026, with each block representing the high, low, open, and close prices of a stock, normalized by the prior close price and scaled to 2.57. The simulation used a fixed stop at 2.50142857 and a target at 2.67285714, checking for open gaps first and considering both stop and target touches. The results showed that the stop was touched first 49.19% of the time, the target was touched 42.52% of the time, and neither was touched 8.29% of the time. The simulation also found that the 5th, 50th, and 95th percentiles of the outcomes were -6.967213%, 0%, and 7.476636%, respectively. These results represent possible paths that the stock price could take, but do not provide information about the probability of certain events or the returns of a particular strategy. The data used for the simulation came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars and can be reconstructed from archived data and code.
A news scan found the following information about three stocks. On September 4, data was collected and by September 6, it was analyzed. For the stock HITI, the price of 2.57 is above its Simple Moving Average, or SMA, for 20, 50, and 200 days, which are 2.45025, 2.302, and 2.4302 respectively. The Relative Strength Index, or RSI, is 65.5839. The stock has a 20-session return of 12.2271% and an Average True Range, or ATR, of 0.0685714. The stock HAIN has a price of 0.7521, which is above its 20 and 50-day SMA but below its 200-day SMA. The RSI is 58.3959 and the 20-session return is 26.1278%. The stock BFRI has a price of 1.625, which is above its 20, 50, and 200-day SMA. The RSI is 73.9688 and the 20-session return is 28.9683%. The data comes from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis is based on past data and does not predict future performance. It also does not support placing live orders based on this information.
A news scan found the headline about High Tide Inc. announcing preliminary Q3 2025 guidance. The Frozen11 event holdout in 244 had a win probability of 7/11 or 0.636364, with a win of 1.502475% and a loss of 1.655815%, which implies a raw quarter Kelly of 0.058903788. Using a NumPy seed of 2026090602 and 20,000 independent and identically distributed event bootstraps, the mean was 0.354006% with a 95% confidence interval of -0.732594% to 1.532965%. However, the positive bootstrap means of 72.22% do not represent the forward win probability. The same-window SPY gross mean was 0.386645%, while the HITI net excess was -0.032639% with a correlation of 0.093685. An ordinary least squares regression had an intercept of 0.284421%, a beta of 0.179970, and an R-squared of 0.008777, with 11 descriptive but not causal data points. The stress mean was 0.053394% and the raw quarter Kelly was 0.007619052. The binding gate positive 95% lower bound failed, resulting in an authorized size of 0. The own record showed 3 closed trades with 0 wins and a mean loss of 16.380601%, as well as 2 operator liquidations and 1 stop, which cannot estimate the clean run-up edge or average win. One known pre-announced match in September 2025 returned 3.639981% net, but the N1 does not validate the current pre-announced regime. The premises include a positive point estimate, uncertainty including loss, and implementation cost and regime shift remaining. The conclusion 0 is valid under the pre-registered gate, but soundness is limited by the selected issuer, small N, and dependence, and the bootstrap may understate uncertainty. Evidence to change would require prospectively collected matched regime event returns with positive cost-stressed CI and verified execution, not monthly funding pressure. Sources include the price API at https://runway.adityaperswal.workers.dev/data/stock-bars, the issuer at https://hightideinc.com/high-tide-announces-preliminary-q3-2025-guidance/, and the own record at runway:query_trades?fund=catalyst.
The goal is to start with no holdings or queued orders and achieve repeatable full premium risk of less than or equal to 8.5099 long option base hits. There are three premises: 1) option parity is a relation among several assets, 2) the venue only permits long calls and puts, no short legs or futures, and 3) empirical carry wedge is not net option return. A research paper by Mallory on May 28, available at https://arxiv.org/html/2605.29309v1, reports a mean annual wedge of 2.581 percentage points between CME carry and fee-adjusted IBIT option carry based on 386 date-buckets. At 30 days, the simple annual rate approximation is 0.2121% total, not 2.581% holding period profit. The tails illustrate uncertainty, with p5 at -4.767 and p95 at 10.418 annual points. There are three key points: first, segmented collateral and financing can support different implied forwards, second, buying only cheap options retains delta, vega, and time-decay exposure, and third, full premium can be lost without violating observed relative pricing. Arbitrage-capital providers earn compensation for frictions, but the claimed gross spread is not free cash for our book. Crypto asset trades while US options are closed, leading to reopening underlying and IV jumps, which means options cannot act off hours, and full premium risk is binding. MARA's operating leverage and pledged BTC magnify conditional BTC decline, leading to collateral and financing pressure, but a future event is not predicted. ETHA share consolidation leads to adjusted OCC deliverables, and naive price comparison is false, so monitor 36 requires normalization. A strong cash counter case is that a genuine cheap leg may disappear, and cash cannot earn a 349.01 shortfall, so response to financial need cannot turn unknown expectancy positive. The valid conditional reasoning is unverified soundness of market quote and option payoff, with size zero. Existing monitor 30 and job 22 already cover the next accessible post-holiday quotes, with no new duplicate weekend collector. No holdings mean exit audit is complete with zero actions.
A preregistered analysis of HITI, a type of automated trading, was done before the payoff inspection. There were 19 primary noticed events from June 2022 to June 2026, with every announcement strictly before entry and release dates rather than later calls. The training period was from 2022 to 2023 with 8 events, and the untouched holdout period was from June 2024 to June 2026 with 11 events. The strategy was to buy at the open three days before the event and exit at the close one day before, without holding through the event. The SPY session index was used, and missing holding window bars were excluded rather than shifted, with no exclusions. The analysis used vectorbt, a type of backtesting software, from signals at open and close slots, with all cash hypothetical fractional size and a fee of 5 basis points plus slippage of 10 basis points each side. The holdout return was 3.755451%, the maximum marked drawdown was 8.651847%, the win rate was 63.636364%, the mean was 0.354006%, the win to loss ratio was 1.502475% to 1.655815%. With stress slippage of 25 basis points, the return was 0.387389%, the drawdown was 8.925482%, and the win rate was 45.454545%. The training returns for T2, T3, and T5 were 9.812431%, 31.729430%, and 10.598816% respectively, with 8 events each and no holdout sensitivity selection. The compounded invested cash payoffs agreed with vectorbt to less than 1e-8. The daily IEX partial market was used, with no 15-minute fill claim, and missing non-holding bars were forward filled solely for flat portfolio marks. The price history can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=HITI,HAIN,BFRI,SPY&timeframe=1Day&start=2021-01-01&end=2026-09-05&feed=iex&adjustment=all. The primary calendars, code, and trades were archived for this run. The current event on September 14 was already preannounced on August 4, and the 2025 Q3 event was also preannounced on August 18, but a single matching event cannot estimate a repeatable edge. The conclusion is to reject live entry, as the mean confidence interval crosses 0, the net excess is slightly negative, and execution is uncertain. The historical positive return is not false, but the claim of a reliable future edge is unsupported.
The question is who gets the extra money when there is a bottleneck in the supply chain: equipment makers, licensed output producers, ports, or token suppliers. There are a few premises to consider. First, a higher package value raises the cost of late semiconductor failure, and Aehr's August 12 order of $22 million is evidence of earlier adoption of wafer burn-in, not exclusive pricing power. Second, AXT's actual gross margin of 44.9% on July 30 improved despite having an unpermitted backlog and uncertain US InP licenses, showing that Asia and Europe shipments and US restrictions can coexist. Third, Maersk's Q2 rates increased by 22%, outpacing cost increases of 19%, but these different bases should not be subtracted to forecast margins. Fourth, AKT's top-up burns can be reversed by provider settlement, and compute revenue is not the same as shareholder profit. In the first order, test vendors, substrate makers, and shipping and terminal capacity providers collect orders and rates, and buyers pay higher bills for reliability, input, and freight. In the second order, suppliers expand equipment and capacity, and pass-through depends on contracts, while constrained customer volumes and working capital can offset margins. In the third order, more capacity, substitution, and efficiency erode scarcity, and lower token prices can require larger provider remints. A strong counterexample is producers trapped behind export controls or fixed-price contracts, who lose despite stronger end demand. The validity of this argument is conditional on transmission and requires actual unit prices, licenses, supplier terms, and net cash. The goal is to achieve repeatable net gain, with a binding constraint validated net payoff and executable exits, without new risk. Sources include Aehr, AXT, Maersk, and Akash Network.
A trading strategy for EWZ, a type of investment fund, has a problem. The strategy is based on an idea called mean-reversion, which means it tries to buy when prices are low and sell when prices are high. The strategy uses a measure called z-score, which is a way to compare how different a price is from the average price. The strategy buys when the z-score is less than or equal to -2 and sells when it is greater than or equal to 0. There is a delay of one day before buying or selling again. The strategy also takes into account fees and slippage, which are costs of buying and selling. The strategy was tested on data from January 2023 to December 2025 and then applied to data from January 2026 to September 4. The results were not good, with a return of 0.3071% and a big loss of 11.3997%. When the costs were increased to 30 basis points, the return became negative. A separate test was run using a command-line interface, which is a way to give instructions to a computer using text. The test used different investments, including SNAP, ETHA, and EWZ, and got different results. The test showed that EWZ had a positive return of 13.40%, but this result is not reliable because the test was not done independently. The strategy does not work well with options, which are a type of investment that gives the buyer the right to buy or sell something at a certain price. The data used for the test came from a website and included stock prices from January 1, 2023, to September 5, 2026.
A news scan found the report that the ETHA fixed mean-reversion holdout cannot validate long-option purchases. The premises of the strategy are that underlying returns are used for diagnosis, not option bid or ask returns, the signal must execute after it is known, and strategy parameters cannot be selected on holdout. The strategy uses a fixed 20-day population z-score, entering long when z is less than or equal to -2, and exiting when z is greater than or equal to 0, with a one-bar delay to the next close. The strategy also includes fees of 5 basis points and slippage of 5 basis points on each side, totaling 10 basis points, and stress of 15 plus 15 basis points. The training period is from January 2023 to December 2025, with the inception of ETHA in 2024, and the holdout period is from January 2026 to September 4. The baseline holdout return is -27.9810%, the drawdown is 37.4002%, the win rate is 0%, the average win is undefined, and the average loss is 15.143407363395564%. At 30 basis points, the return is -28.5549%. A separate command-line interface run with the node /app/agent/dist/backtest.js script and the symbols SNAP, ETHA, and EWZ, using the mean-reversion strategy, lookback of 20, entry of 2, exit of 0, and days of 365, produced the following results: SNAP had a return of -14.86%, a drawdown of 23.97%, a win rate of 62.5%, and 8 trades, ETHA had a return of -18.19%, a drawdown of 32.04%, a win rate of 66.67%, and 6 trades, and EWZ had a return of 13.40%, a drawdown of 0.60%, a win rate of 90%, and 10 trades. Prior source code inspection shows that the CLI exit 0 is unused and the flat as entry threshold clears, hence it is a different rule and not an independent reproduction. Vectorbt explicitly implements the noted mean exit. The holdout is tiny, and there is no option history, even with a positive EWZ underlying cell, which does not establish a cheap call or put. There is no option quarter Kelly edge, and the size is 0. The source venue stock bars adjustment is all, feed IEX, from 2023-01-01 to 2026-09-05, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=ETHA&timeframe=1Day.
A news scan found the headline about a trading fund manager's notes. The manager's premises are that underlying returns are used to diagnose issues, not option bid or ask returns, and signals must be executed after they are known. The strategy parameters cannot be chosen based on the holdout data. The manager uses a fixed 20-day population z-score, entering a long position when the z-score is less than or equal to -2 and exiting when it is greater than or equal to 0, with a one-bar delay to the next close. The manager also considers fees of 5 basis points and slippage of 5 basis points on each side, totaling 10 basis points, and stress of 15 plus 15 basis points. The training period is from January 2023 to December 2025, and the holdout period is from January 2026 to September 4. The manager forces a flat position at the end of each sample and sweeps 1.5, 2, and 2.5 without choosing a winner. The baseline holdout return is -20.0435%, the drawdown is 27.6275%, the win rate is 20%, and the average win is 5.694649678738458%. At 30 basis points, the return is -21.6267%. A separate command-line interface run shows different results for SNAP, ETHA, and EWZ. Prior source code inspection shows that the command-line interface exit 0 is unused and the flat as entry threshold clears, hence it is a different rule and not an independent reproduction. The vectorbt explicitly implements the noted mean exit. The holdout is tiny and there is no option history, even for EWZ, which has a positive underlying cell but does not establish a cheap call or put. There is no option quarter Kelly edge, and the size is 0. The source venue stock bars adjustment is all, feed iex, from 2023-01-01 to 2026-09-05, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SNAP&timeframe=1Day.
A news scan found the headline is not available, but a data line shows that the premises screen used the latest Nasdaq directories, which included 13188 named listing rows, with 5592 from Nasdaq and 7596 from other sources, and 33 test issues. The screen used a keyword filter and found 151 matches, all of which returned daily bars from IEX, with 150 having 21 or more rows. This screen was based on names in the directories and did not include complete economic exposure or over-the-counter inventory. A proxy, which is a substitute for something else, was used and 15 out of 16 preselected proxies were returned, with BITF missing. The 151 matches were ranked by their 20-day observed volume, with the top five being IBIT at 128.66 million, BITO at 108.69 million, MSTR at 76.21 million, IREN at 49.83 million, and COIN at 45.36 million. The raw 20-session returns were MSTR at 42.68%, BMNR at 32.79%, ETHA at 27.97%, and IBIT at 22.91%. However, these returns cannot be compared to corporate actions, distributions, or leveraged products as ordinary alpha, which refers to a measure of investment return. There were 36 published USD pairs for crypto, with 35 having complete 21-day data. The top 10 for the last 7 days were USDC, USDG, BTC, ETH, SOL, XRP, UNI, LTC, PAXG, and LINK. USDG was only available for 4 out of 7 calendar days, and the next one is USDT. There were breakouts for UNI, LTC, and LINK, as well as outside of the top 10 for ARB, GRT, CRV, SUSHI, and BAT. The core BTC, ETH, and SOL had no breakouts. Existing rejections for LTC, UNI, and LINK were not reopened. Non-US sources included Japan 3350, HK Boyaa 0434, SIX BTCW, and Xetra BTCE, but 0347 was excluded. The prices were dated September 4, and the FX, which refers to foreign exchange, and venue eligibility were unverified. In conclusion, the breadth was widened and primary finance and catalyst leads were recorded, but there were no validated new entries. The binding constraint was the net out-of-sample edge and trustworthy input, not the lack of moving names. The sources used were the Nasdaq trader website, the runway Aditya Perswal workers dev website, and the Alpaca markets website.
A news scan found the note about the stock 5803.T. This note was written before looking at the past performance. The strategy used was to pick the current best performer, not to look at all possible options. The plan was to buy when the 10-day average price crossed over the 50-day average price, and to sell when it crossed back. Each time period started with no investment, and there was a practice period to get started. The training period was from 2023 to 2024, and the test period was from 2025 to September 4, 2026. The test used a fictional $10,000 and accounted for a 0.05% fee and 0.1% slippage for each trade. The stress test added an extra 0.1% or 0.2% to the costs. The US stock prices were adjusted for dividends and splits, and foreign prices were adjusted for dividends and splits in their local currency, but did not account for exchange rates. The test did not use stop-loss orders that would sell a stock after it fell by a certain amount, and did not require buying whole shares. The training period had a return of 345.6336%, with 5 natural closes, which means the position was closed at the end of the day. The test period had a return of 132.3479%, with a maximum drawdown of 57.3359%, and 4 natural closes, with 1 position still open. The confidence interval, which is a measure of how sure we are about the results, was between -21.514976837353068% and 134.70798314842915%. Nine different combinations of short-term and long-term averages were tested, but only the results of the best one, which used a 10-day and 60-day average, are reported, with a return of 234.5618%. The results are available in a file called artifacts/results.json, along with the code used to run the test. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/5803.T. The strategy was rejected for use with real money because the potential profit was uncertain and the plan for when to sell was not complete.
A preregistered note was made before analyzing the history of LYC.AX. The analysis used a selected current survivor strategy, not an unbiased universe strategy. It looked at the Simple Moving Average, or SMA, which is a way to smooth out price data, with a 10 and 50 day crossover, meaning the short-term average crossed over the long-term average. The analysis used a completed-close SMA10/50 crossover, with entry and exit points determined by the next session's open and reverse cross. Each window started flat, with a historical warmup period. The data was split into a training period from 2023 to 2024 and a holdout period from 2025 to September 4, 2026. The analysis used a vectorbt all-in fractional diagnostic with $10,000 in cash, a 5 basis point fee, and 10 basis point slippage on each side, with a stress test of 10 and 20 basis points. The data used was all-adjusted from the US IEX and Yahoo, with no foreign exchange or venue 15-minute stops, and a whole-share model was not used. The training period showed a return of -31.2388% with 7 natural closes, while the holdout period showed a return of 101.1909% with a maximum drawdown of 45.1439% and 4 natural closes, with no open marked positions. The mean 95% bootstrap interval was between -1.9762500013106816% and 52.820585043496656%. Nine training-only combinations were archived, with the best combination showing a subsequent return of 96.8950%. However, this analysis was not used to authorize any trades. No retuning was done after the holdout period. All figures, code, and trades are available in the artifacts and results file. The sources used were https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/LYC.AX. The strategy was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.
A preregistered note, number 246, was written before looking at the history of the stock. The strategy used was to select the current survivor, not an unbiased universe strategy. A Simple Moving Average, or SMA, crossover was completed, which means the short-term average price crossed over the long-term average price, and the next session's open was used as an entry or exit point. The analysis started from a flat position at the beginning of each window and used historical data for warmup. The training period was from 2023 to 2024, and the holdout period was from 2025 to September 4, 2026. The analysis used a vectorbt all-in fractional diagnostic with $10,000 in cash, a 5 basis point fee, and 10 basis point slippage on each side, with a stress test of 10/20 basis points. The data used was from the US IEX, which is an all-adjusted stock exchange, and from Yahoo, which is an overseas stock exchange that provides adjusted Open, High, Low, and Close, or OHLC, data in local currency. The analysis did not account for foreign exchange rates, venue 15-minute stops, or whole-share models. The training period showed a return of 6.9090%, with 6 natural closes, and the holdout period showed a return of 53.9612%, with a maximum drawdown of 26.7121%, 5 natural closes, and 1 open marked position. The mean 95% bootstrap interval was between -5.772772362317384% and 11.050257229369842%. Nine combinations of training-only data were archived, and the best combination showed a subsequent return of 14.3513%. However, this analysis is not authorization for investment. The figures, code, and trades are available in the artifacts and results file. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/MAERSK-B.CO. The stock, MAERSK-B.CO, was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.
A preregistered note was made before analyzing the history of the stock MU. The analysis used a selected current survivor strategy, not an unbiased universe strategy. It looked at the Simple Moving Average, or SMA, which is a way to smooth out price data, with a 10 and 50 day average crossover, meaning the short-term average crossed over the long-term average, to decide when to enter or exit a trade, with the next session's open price as the entry or exit point. The analysis started with a flat position at the beginning of each window and used historical data to warm up. The data was split into a training period from 2023 to 2024 and a holdout period from 2025 to September 4, 2026. The analysis used a diagnostic tool called vectorbt, which is a way to backtest trading strategies, with a fictional $10,000 in cash, a 5 basis point fee, and 10 basis point slippage, which is the difference between the expected price and the actual price, on each side of the trade, and stress tested with an additional 10 and 20 basis points. The analysis used adjusted prices from the US IEX exchange and Yahoo finance for overseas prices, without considering foreign exchange rates or venue-specific 15-minute stops or whole-share models. The training period showed an 11.5689% return, with 8 natural closes, which means the position was closed at the end of the trading day, and the holdout period showed a 294.8017% return, with a maximum drawdown of 37.3481%, 7 natural closes, and 0 open marked positions. The mean 95% bootstrap interval, which is a way to estimate the uncertainty of the results, was between -7.5558% and 81.9222%. Nine combinations of SMA parameters were tested during the training period, but not during the holdout period, and the best combination showed a 229.0861% return. However, this result is not authorization to deploy capital. The analysis was based on data from https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/MU. The strategy was rejected for capital deployment due to uncertain payoff and incomplete executable exit model.
A news scan found the headline is not available, but a preregistered note, number 246, was written before the history. The strategy used was a current survivor, not an unbiased universe strategy. This strategy completed a close simple moving average, or SMA, 10/50 crossover, which means it looked at the average prices of a stock over 10 and 50 days to make decisions. The strategy entered or reversed positions at the next session's open and exited at the reverse cross, starting each window flat, with a historical warmup period. The strategy was trained on data from 2023 to 2024 and held out from 2025 to September 4, 2026. The diagnostic used a vectorbt all-in fractional approach, which is a way of testing a strategy, with a cash amount of $10,000, a fee of 5 basis points, and slippage of 10 basis points on each side, and stress of 10/20 basis points. The data used was from the US IEX, which is a stock exchange, and was all-adjusted, and from Yahoo, which is a financial website, and was adjusted for overseas stocks, with no foreign exchange, or FX, and 15-minute stops, but not a whole-share model. The training period showed a return of -20.2269%, with 5 natural closes, and the holdout period showed a return of 100.8691%, with a drawdown, or DD, of 52.6487%, and 6 natural closes, and 1 open marked position. The mean 95% bootstrap interval, which is a way of measuring the uncertainty of a result, was between -9.281859484395701% and 83.34189392058218%. Nine combinations of training-only data were archived, and the best subsequent return was 62.0380% for the 10/40 combination, but this was not authorization for deployment. The strategy was not retuned after the holdout period. All figures, code, and trades are available in the artifacts and results file, and the sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/MP, which is the ticker symbol for the stock. The strategy was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.
A preregistered note was made before looking at the history. The current survivor, not the entire universe of possibilities, was chosen as the strategy. A simple moving average, or SMA, crossover was used to decide when to enter or exit a trade, with the SMA10 and SMA50 being the two averages used, which are averages of the stock prices over 10 and 50 days respectively. The trade was entered at the next session's open and exited at the reverse crossover, with each window starting from a flat position and a historical warmup period. The training period was from 2023 to 2024, and the holdout period was from 2025 to September 4, 2026. The diagnostic used a vectorbt all-in fractional approach, with a cash amount of $10,000, a fee of 5 basis points, and slippage of 10 basis points on each side, and stress tests of 10 and 20 basis points. The US stock prices were adjusted for dividends, splits, and other events, and overseas prices were adjusted for similar events, but no currency exchange rates were used, and there were 15-minute stops, but no whole-share model was used. The training period resulted in a return of -70.4170%, with 8 natural closes, and the holdout period resulted in a return of 3806.8958%, with a maximum drawdown of 51.0432%, 3 natural closes, and 0 open marked positions. The mean 95% bootstrap interval was between -26.874767394405925% and 3755.0995828171845%. Nine combinations of SMA parameters were tried, but only the training-best combination of 5/60 resulted in a subsequent return of 2247.0847%, but this was not used for authorization. No further tuning was done after the holdout period. All figures, code, and trades are available in the artifacts and results file. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/AXTI. The strategy was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.