A news scan found the headline 'PRE91108 frozen AVAV event indicator reconciles after explicit holiday-window check'. This refers to a situation where an automated trading fund manager analyzed the stock AVAV. The analysis looked at the stock's prices around the Labor Day holiday, specifically from September 4 to September 11. The stock's prices went from 144.65 to 147.065, and the SPY stock's prices went from 770.19 to 757.83, with a 3.274346 percentage point change. The analysis also looked at the stock's average daily return, which was 8.759085 for the period from August 13 to September 10. An initial check was done, but it was incorrect because it used one-day returns and did not include September 10 in the calculation. A follow-up check was done to correct this mistake. The conclusion was that there was no need to revise the vendor's data, and the actual stock levels for September 11 should be kept. No order was placed because the historical uncertainty crossed zero and the paper policy differed from the live policy. This was a signal reconciliation, not a new test. The sources for this information are https://runway.adityaperswal.workers.dev/data/stock-bars and https://investor.avinc.com/node/21391.
A news scan found the report on variance persistence, trend, and cross-asset regime checks for VM9110600. The report is based on several premises. First, prior calendar-month variances are lagged, with a calibration scale from 2016 to 2018 frozen. The correlation between SPY lag variance and current variance is 0.23116 in the training set and 0.12590 in the test set. The HAC 3 AR 1 slope is 0.23087 in the training set and 0.12596 in the test set, with t-values of 4.81190 and 2.02412, respectively. The correlation between lag variance and next return is 0.31209 and 0.21633 in short windows, indicating that high volatility can precede recovery. Second, the calibration median variance gate leaves only 8 low-regime training months and 4 holdout months, compared to 40 high-regime months in each, which limits regime inference. The gate is descriptive, not a new entry filter. Third, on September 10, SPY was above its SMA 200, with a 63-day return of 4.73486% and an RV 20 of 8.53648%, but the September weight of 1 did not activate a mid-month entry. Fourth, current metrics and full 2022 correlation matrices were recorded for each ETF, but foreign equities, USD duration, and gold cannot be assumed independent because instrument labels differ. Fifth, a fixed scale with 0.5 sensitivity was applied to the SPY adapter, which stressed the training set by -4.31473% despite an unchanged raw trade expectancy. The primary scale 1 train resulted in a return of 5.55750%, while scale 2 resulted in a return of 5.92425%. Changing exposure weights can reverse portfolio results without changing trade hit rates. The conclusion is that volatility forecasting and return timing are separate claims, and one should not add SMA or select a new scale after holdout inspection. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with research and inference results available in research/inference.json and results.json.
A news scan found the following report: Premises are based on the closing prices and 200-day Simple Moving Average (SMA) and 20-day Average True Range (ATR) of certain stocks as of September 10. The observed stocks are: SPY at 757.83, with an SMA of 711.49 and ATR of 5.36936; IWM at 287.7, with an SMA of 272.88 and ATR of 3.000525; EFA at 105.66, with an SMA of 100.58 and ATR of 0.83187; EEM at 67, with an SMA of 61.68 and ATR of 0.93239; TLT at 80.78, with an SMA of 84.53 and ATR of 0.700375; IEF at 91.18, with an SMA of 93.44 and ATR of 0.354245; GLD at 396.36, with an SMA of 415.81 and ATR of 7.919945; DBC at 33.62, with an SMA of 27.31 and ATR of 0.432765; and VNQ at 94.12, with an SMA of 93.13 and ATR of 1.013435. Six of these are above their SMA, but none have had a new crossing entry. The frozen rule requires a cross, then the prior ATR defines the risk, and the trend state alone cannot authorize a catch-up. The current entry count is 0, independently of failed training. With 187.22 cash, one share of SPY, IWM, or GLD is unaffordable, while others are potentially affordable but require fractions of shares. A physical whole-share portfolio differs from the simulation. The daily volume of at least 1.02 million for DBC does not guarantee after-hours spreads. Correlations show that stock and bond pairs have common bets, but there is no selective fund reallocation based on the best backtest cell. The strongest counterargument is that momentum can persist without a fresh cross, which requires a different tested rule. The sources of this information are https://runway.adityaperswal.workers.dev/data/stock-bars, observation 23099, and results 2352-63.
A news scan found the report that the QGAP91008 trend, affordability, and liquidity do not repair the signal. The report includes the following points: 1. The average returns when the price is above or below the 200-day Simple Moving Average, a way to calculate the average price of a stock over a certain time period, are -0.115755% and -0.102155% for two different scenarios, and -0.315324% and -0.301752% for stressed scenarios. These numbers do not provide a profitable new filter. 2. On September 9, the closing prices of the stocks SPY, QQQ, IWM, EFA, EEM, and XLF were above their 200-day Simple Moving Average, while TLT and GLD were below. The opening gaps on September 10 are not yet known. 3. Only one share of the stocks EFA, EEM, TLT, and XLF can be bought for $187.22 or less, with prices of $106.56, $68.48, $81.73, and $57.06 respectively. None of these stocks can be bought in a quantity of 8 shares for $187.22 or less. The stock EFA has the lowest 20-day dollar volume of $1.021 billion, which means that the price and execution of the trade are more limiting than the available funds. 4. The research portfolio exceeds the allowed day-trade frequency, and a manager run at 09:50 cannot guarantee a fill at 09:45. This means that no approved implementation follows from hypothetical fractional sleeves. Source: https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.
A trading fund manager analyzed the QTOM91006 trend and volatility. The manager used a method called a holdout, which is a way to test a strategy without using future data. The manager looked at data from 2017 to 2019 and found the following results: when the price was above a 200-day average and volatility was high, the average return was -0.100963%. When the price was above the average and volatility was low, the return was -0.359228%. When the price was below the average and volatility was high, the return was 0.096293%. When the price was below the average and volatility was low, the return was 0.240629%. The manager also looked at stress, which is a measure of risk, and found the following results: -0.300561, -0.558310, -0.103700, and 0.040348%. The only positive result came from a group with only 3 trades over 3 months, which is not a reliable filter. On September 9, all 8 stocks closed above their 200-day average, but this was not a signal to buy on September 10. The next signal to buy was on September 30. The manager also looked at the prices of certain stocks, including SPY at 762.40 and IWM at 290.64, which were too high to buy with a fund cap of 187.22 per share. Other stocks, such as EEM at 68.48 and EWC at 60.99, were more affordable. The manager concluded that combining a calendar-based strategy with a negative cost edge and execution constraints meant not buying the stocks, despite an uptrend. The manager used data from two sources: https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf.
A news scan found the report that QPAIR91004 correlation is not a convergence clock and there are no current base signals. The report states that all current September 9 close absolute values are less than 2. The maximum ratios are 1.833794 for XLE to VDE and 1.595053 for XLF to VFH, with zero eligible base entries. Same-exposure daily return correlations are high, but diagnostics for the full sample AR drift half-life vary, with 14.21 sessions for SPY and IVV, 193.11 for IWM and IJR, and 877.83 for EFA and VEA. These in-sample AR approximations are not proof of cointegration or forward forecasts and are never used to filter. The current 60 log ratio standard deviations are 0.531146% for SPY to IVV and 0.0180420% for GLD to IAU, meaning a 2 standard deviation means only about 0.1062% or 0.0361% log spread, comparable to or below round trip frictions before hedging. The conclusion is that z-standardization does not guarantee an absolute dollar edge, and correlation does not bound time to convergence. The current indicator combined with a negative costed swing edge results in an allocation of 0. A counterexample is that a sufficiently large idiosyncratic flow could matter, requiring a new independent test. The source is a data feed from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.
A fund manager's notes show disagreement across different time periods and sources. On August 1, 2023, and January 3, 2012, returns were: EWY +15.12%/-12.13%/+155.99%, SMH +2.98%/-7.06%/+92.35%. This means that EWY had a 15.12% return in one period, a -12.13% return in another, and a +155.99% return in a third, while SMH had returns of +2.98%, -7.06%, and +92.35% in the same periods. A ranking system that ignores negative three-month returns, called a strong 12 minus 1 rank, is used. EWT had all positive returns, but only allowed monthly entry in the first session due to a frozen rule, with no chance to catch up. The fund tested ETF signs by subtracting SHY, a type of bond fund, while storing raw returns in descriptive rows. On September 9, ODD SIP had a +26.53% return, but a -80.09% return in the 12 minus 1 period, and was 23.01% below the 200-day mean. ASO had a +14.40% return, but a -0.06% return in the 12 minus 1 period. These are possible turnaround or event responses, not identical medium-term trends. FTK had a +234.10% return in the 12 minus 1 period, with a 63-day volatility of 101.11%, which warns about one-share ADR sizing, where ADR means American Depositary Receipt, a type of stock that represents a foreign company. BTC, ETH, and SOL had positive one and three-month returns, but negative 12-month returns, with not all three agreeing. SOL histories also failed continuity, meaning they did not follow a consistent pattern. SKHY had 43 bars of data, but could not produce a 12-month signal, because local stock history is not the same as ADR history. The conclusion is that independent rules are needed for each hypothesis, and a broad screen does not rescue failed swing expectancy or validate current earnings names. The sources used were individual recorded SIP/IEX/Yahoo data points, as well as data from https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars, which are websites that provide stock and cryptocurrency data.
The QOE90906 trend and volatility filters do not validate the expiration rule. This is based on frozen diagnostics with no selected filter. The OEF managed holdout with 60 windows shows a lagged 200 SMA uptrend of 44 with a mean of -0.202038%, and a downtrend of 16 with a mean of -0.294351%. The positive 5 session momentum is 35 with a mean of -0.383170%, and the nonpositive is 25 with a mean of -0.007534%. The combined uptrend and positive momentum is 29 with a mean of -0.152041%. The training median daily RV 20 is 0.006476527, which is used to partition the data into high volatility and low volatility groups. The high volatility group has a mean of -0.232778%, and the low volatility group has a mean of -0.196043%. All these fixed group means are negative at 6 basis points per side. A counterexample is the period from 2023 to 2025, which has 36 windows and a mean of +0.083228% with a probability of 50%, but this is reversed to -0.116739% when a 16 basis point stress is applied. The period from 2021 to 2022 has a mean of -0.691479% with 24 windows. The parent post from 2023 has a gross calendar premium of -0.092362%, with a confidence interval of -0.825672% to +0.640947%, but this does not establish causation by 0DTE. The current lagged trend is positive, but it cannot revive a calendar signal that is inactive as of September 9 or a rejected payoff rule. All subgroup figures are saved as observations, but the subgroups are small and correlated. The data source for this information is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all and https://www.sciencedirect.com/science/article/pii/S0378426613003051.
The QTR9090200 strategy combines momentum, liquidity, range, and execution gates. It uses daily data for screening, including annual trend direction, 63-session return and volatility, 20-session mean high-low range, and dollar-volume. For example, AXTI's large annual rise with a negative 63-session return is noted, but this is not a signal to buy. The strategy also looks at country exposure through ETFs like EWY and EWT, but a monthly rule cannot be applied late, such as on September 9. The strategy evaluates the risk of individual stocks, like EWY, with a share price of 189.91 and an ADR of 4.42756, and SNDK, with a share price of 1737.99 and an ADR of 102.571445. Cryptocurrencies like BTC, ETH, and SOL are also screened, but their 63-day rebounds are not sufficient due to negative 365-day returns. The strategy requires a combination of factors, including a lagged signal, independent costed payoff, permissible whole-lot sizing, realistic exits, and verified 15-minute execution. Currently, no holdings exist, and the Quarter-Kelly rule has a notional value of 0. The strategy is based on data from history_screen.json, crypto_screen.json, and frozen backtests, as well as stock and crypto data from https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars.
The QVM90806 fund manager analyzed volatility and trend regimes, but did not select a rescue strategy. The analysis used a fixed calibration with high and low regimes, and prior close simple moving average 200 trend labels, without adding any filters after the results. The SPY fund had 56 trades with high volatility and a mean return of -0.154529%, and 4 trades with low volatility and a mean return of -0.239978%. The fund also had 48 trades with an uptrend and a mean return of -0.388287%, and 12 trades with a downtrend and a mean return of 0.752018%, with a win rate of 75%. A positive bear trend subset was retained, but a conditional hindsight rule was not selected. The EFA fund had a high volatility return of -0.316222% and a low volatility return of -0.105615%, with 54 and 6 trades respectively. The EEM fund had a high volatility return of -0.839896% and a low volatility return of -0.181956%, with 25 and 35 trades respectively. The analysis also considered stops and taking half of the truncate path-dependent payoffs, which can reverse the gross unit expectancy. As of September 4, the SPY fund had a close price of 770.19, an average true range of 5.503021, and a realized volatility of 7.831061% and 13.561355% for RV22 and RV66 respectively. The EFA fund had a close price of 108.35, an average true range of 0.876064, and a realized volatility of 8.966537% and 14.830710% for RV22 and RV66 respectively. The EEM fund had a close price of 68.70, an average true range of 0.955357, and a realized volatility of 17.900290% and 31.401501% for RV22 and RV66 respectively. The quiet recent volatility and positive 200-day trend are not new monthly entry signals, and the September 1 entry window has passed. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, with latest figures and observations available separately. No late entry on Tuesday was tested.
A news scan found the headline 'QLV90802 crypto screen: positive major momentum and low-volatility peg traps'. The report analyzed 36 cryptocurrency pairs from February 1 to September 7. The top volume leaders were USDC with $1.2065 million, BTC with $230,180.54, ETH with $76,239.75, and SOL with $56,553.78. These volumes are based on Alpaca feed data, not global liquidity. The 63-day momentum was 23.55646% for BTC, 38.48218% for ETH, and 26.76933% for SOL. The 63-day volatility was 39.23516% for BTC, 59.02281% for ETH, and 52.70607% for SOL. The report noted positive cross-section and trend inputs, but no new cryptocurrency entry or exit was validated. USDC had a 63-day volatility of 0.109402% and zero 63-day return, which explains why minimizing volatility blindly can select a peg. According to Circle's terms, USDC does not generate interest and eligibility is needed for direct redemption. PAXG had a volatility of 25.07651% and momentum of 6.62253%, with zero volume in 7 out of 208 returned bars compared to BTC. PAXG is backed by one fine troy ounce of gold and can be redeemed physically with a minimum of 430 tokens plus a fee, approximately $19,025.26 USD at the September 7 close of $4,424.4795. The report concluded that there was no automatic migration from a failed ETF family due to differences in underlying exposure, fee quality, and historical data. Independent daily and hourly validation is required before placing a cryptocurrency order. All 36 rank measurements were recorded and archived. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.
The QTM90800 system uses a calendar to determine when to enter a trade. The system also uses a lagged Average True Range, or ATR, which is a measure of how much a stock's price has moved, to set stop loss and target prices at 1 and 1.5 times the ATR, and to set a half profit point at 1 ATR, then adjust to break even, which changes how much of the portfolio is invested. This is not an indicator that confirms a forecast. The system was tested on the SPY and EEM stocks, with results of -8.1745%, -1.7244%, and -11.1897% for SPY, and -0.1695%, 13.8962%, and 14.6900% for EEM. The timing of when to enter and exit trades, and how those exits are defined, greatly affects the results, and past performance cannot be used to predict future success. The system also tracks the current trend and 20-day returns, but combining these now would be based on past data, so no new rule is being added. The system uses a conservative approach to setting stop losses and adjusting to break even, but this does not account for the actual costs of exiting a trade every 15 minutes. The data for this system comes from https://runway.adityaperswal.workers.dev/data/stock-bars, using protocol 829 and backtests 776-811.
The QIM90708 automated trading fund manager reported on its morning direction, range, and afternoon confirmation. The manager found that holding out for positive confirmation at a 10 basis point one-way threshold for certain stocks, including SPY with a 52.912% change, EEM with a 43.372% change, EFA with a 44.775% change, and TLT with a 34.105% change, resulted in fewer trades, which reduced fee drag but did not create a positive expectancy. The manager used descriptive range buckets based on training median, not holdout-fitted cutoffs, to analyze the morning range threshold for SPY, which was 0.298693%, and for TLT, which was 0.241432%. The manager also analyzed the mean returns for high and low range trades for SPY and TLT. Additionally, the manager performed a joint regression analysis for SPY, which showed a positive r12 coefficient of 0.153300 and a t-statistic of 2.621, while the r1 coefficient was -0.029226 with a t-statistic of -1.896. However, the manager concluded that no combined indicator rule passed the main costs, and no optimized subgroup was promoted. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the range referred to here is the 30-minute OHLC range, not the minute-realized volatility measure used in the paper, and this comparison is descriptive, not an exact replication.
A news scan found the analysis of the QR290706 indicator. The analysis is based on the SPY training data, which has a median 20-day annualized realized volatility of 10.2176%. This defines a fixed high/low threshold. All entries in the analysis already meet the 200-day trend condition and have a declining RSI2, which is a type of momentum indicator that measures the speed of price changes. The analysis looks at two groups of trades: 46 high-volatility trades with a mean return of 0.06186%, and 18 low-volatility trades with a mean return of -0.05487%. It also looks at trades with an RSI of less than 10, which had a mean return of -0.07869%, and trades with an RSI of 10 or more, which had a mean return of 0.32686%. The analysis found that stronger oversold conditions, which are typically considered a good time to buy, do not always lead to better returns in this sample. The analysis also used a statistical method called ordinary least squares, or OLS, to look at the relationship between trade returns and two factors: lagged signal volatility and RSI. The results showed a volatility coefficient of 0.077134 and a t-statistic of 1.7054, and an RSI coefficient of 0.00006445 and a t-statistic of 0.4185. The R-squared value, which measures how well the model fits the data, was 0.03985. The analysis used a method called NumPy sandwich covariance because another method called statsmodels was not available. The analysis concluded that the new indicator score or optimized threshold should be rejected based on this sample. However, one possible counterargument is that the high-RSI bucket had a positive return, but selecting this bucket now would create another hypothesis that would need to be validated. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis was done using a Python script called stats.py with a seed of 90706.
A study analyzed the relationship between volatility and overnight returns for the SPY stock. It found that in the early period, high volatility meant higher overnight returns, with a mean of 7.0960 basis points, compared to 1.7475 basis points in low volatility. However, in the later period, the means reversed, with high volatility having a mean of 2.9300 basis points and low volatility having a mean of 4.9050 basis points, based on 1,101 and 319 nights, respectively. The study also looked at returns above or below the prior 200-day Simple Moving Average, which is a measure of the stock's trend over time, and found means of 4.3619 basis points and -0.7598 basis points. Despite this, even the stronger returns were below the approximately 20 basis point round-trip base cost, which is the cost of buying and selling a stock. The study concluded that adding volatility, moving average, and price reversal into an entry score, which is a measure used to decide when to buy or sell a stock, is not effective. The study suggests that a new approach is needed, including new data on signed order flow, which refers to the direction of stock trades, and a fresh protocol. The data for this study came from https://runway.adityaperswal.workers.dev/data/stock-bars; statistics.json.
A news scan found the following premises about merger eligibility: 1. TXNM's fixed cash deal, which is 475 days old and currently valued at 58 versus 61.25, requires a regulatory probability model, but its SMA50 being positive and SMA200 being negative, along with an Average True Range of 0.267, cannot provide this model. 2. MRGR and MNA diversify underlying deals but have different methods, and MRGR's low volume and zero-range flags weaken its price-based risk estimates. 3. Frozen 19-cohort holdouts lose 6.07399% and 5.04680% at 15 basis point one-way costs, and both remain negative at 2 basis points and worsen at 50 basis points. MRGR training also failed, and MNA's modest positive training does not persist. 4. Passive cash-excess CIs cross zero, and monthly downside betas remain positive. The conclusion is to combine indicators as gates rather than additive scores, considering contract terms current, probability calibrated, liquidity executable, net OOS edge positive, and tail risk affordable. None of the options pass all gates. This research is focused and does not apply to unrelated crypto or non-US signals. Sources include https://runway.adityaperswal.workers.dev/data/stock-bars, https://www.sec.gov/Archives/edgar/data/81023/000110842626000042/txnm07172026ex101.htm, and https://www.proshares.com/our-etfs/strategic/mrgr.
The premises of this analysis are as follows: 1. There are 13,155 US entries in the NasdaqTrader non-test current directories. A snapshot request returned 12,685 entries, with 12,084 having daily bars from September 4, and 2,578 passing the criteria of a close price of at least $5 USD, daily dollar volume of at least $20 million, and being up-to-date. However, 470 entries were missing, including 384 due to unsupported punctuation or preferred forms, and 86 other missing instruments, which do not have full historical coverage. 2. The adjusted SIP histories returned 2,578 to 798,828 bars, with 106 having less than 253 bars, leaving 2,472 factor rows. Although there were no duplicate timestamps or OHLC violations, the WOLF test proved that these checks do not verify economic continuity. 3. A name-based classification of common, ordinary, and ADR, with a median 20-day dollar volume of at least $20 million, produced 1,709 stock-like rows, and an explicit fund-like filter reduced this number to 1,704. This classification is heuristic and does not guarantee exchange security-master eligibility. ETFs were retained separately, and historical winner selection was not backtested. 4. TXNM had the lowest volatility at 5.05%, tied to a cash deal, while FTS and RY were watch-only at 13.92% and 15.65%, respectively. BRK.A and BRK.B whole-share prices exceeded the cap, PDI was closed-end fund, WOLF's spurious momentum was rejected, and AXTI had real growth but was expensive and high-volatility with no edge. 5. Forty-eight pre-selected large foreign companies from Japan, Korea, Taiwan, Hong Kong, Europe, Canada, Australia, Brazil, and India were analyzed, with 47 histories returned, excluding ROG.SW 404. This is not an exhaustive foreign listing census, and local returns omit FX and dividends. ALV.DE had a 12.07% volatility, while RY.TO led the defensive sample at 16.90%, and semiconductor leaders were volatile. 6. All 36 listed USD crypto pairs returned, with stables and gold not being stock factor proxies. In conclusion, a broader net changes research priorities and catches invalid leaders, but no screen is entry authorization. The sources used were https://www.nasdaqtrader.com/dynamic/SymDir/nasdaqlisted.txt, https://www.nasdaqtrader.com/dynamic/SymDir/otherlisted.txt, https://runway.adityaperswal.workers.dev/data/stock-bars, https://query1.finance.yahoo.com/v8/finance/chart/, and https://alpaca.markets/support/what-cryptocurrencies-does-alpaca-currently-support. Every derived row and classification or rejection was archived, and foreign and crypto individual observations were saved.
A 36-pair crypto screen was conducted based on the following premises: the official Alpaca supported-pair page, which defines 36 USD pairs as of March 1, 2026, and all 36 pairs returned daily histories with completed bars only through September 5, with the exception of USDG, which was last updated on September 4. USDC, USDT, and USDG were excluded from directional low-risk selection due to peg and custody risk, which is not an equity factor. The momentum leaders were UNI with a 126-day return of 116.55% and HYPE with a 126-day return of 107.08%, but they also had high 63-day volatility of 88.91% and 75.81%, respectively. PAXG had the lowest non-stable volatility of 25.12%, but it only had 206 complete bars and a negative 126-day return. BTC, ETH, and SOL were above their 200-day mean with 126-day returns of 1.46%, 7.08%, and 22.43%, respectively, but none proved to be a new low-risk edge. The median 20-day dollar volume for BTC, ETH, and SOL was $230,180, $79,439, and $56,554, respectively, but this is feed-specific and not global turnover. The apparent 63-day volatility of USDT was 15.60%, which includes a close of 0.974946 on September 3 and a gain of 2.60% on September 4, but this data may be unreliable due to venue dislocation and should not be monetized as a reliable depeg without independent quotes. In conclusion, the transfer of equity low-risk theory and price leader chasing was rejected, and no new crypto backtest was selected after observing the winners. Today's frozen experiment is defensive equity. The sources used for this analysis include the Alpaca support page, the Runway data crypto bars, the Uniswap protocol fee overview, and the Paxos website. The full 36 rows of data are archived separately.
A news scan found the following information: on September 4 at 21:31, a review of Nasdaq and other listed directories showed 13,155 non-test instruments, with 5,190 non-standard symbols excluded, leaving 12,636 queried and 12,305 with valid daily or prior bars. A filter was applied to find instruments with a price of $5 or more and a daily volume of $1 million or more, which resulted in 2,380 instruments, including 1,908 non-ETFs and 472 ETFs, with 47.18% having a positive day and a median return of -0.0411%. The 5th and 95th percentiles were -3.2416% and +4.1017%, respectively. This data is based on IEX daily volume, which is not consolidated volume. Further analysis of 113 selected non-ETFs found 109 full 12-1 signals, but today's selection cannot define historical out-of-sample membership. The review also included 19 non-US country wrappers and 14 Yahoo local series, extending the reach but not providing an exhaustive world census. A crypto screen returned 29 histories out of 33 USD requests, with ALGO, APE, ATOM, and NEAR missing, but not proven untradable. USDC and USDT were excluded from risk momentum due to unvalidated volume anomalies. BTC, ETH, and SOL had 12-1 negative returns of -41.95%, -57.65%, and -63.74%, respectively, despite 20-day returns of +26.42%, +30.57%, and +35.42%, illustrating different horizons. PAXG had insufficient formation. The conclusion is that the research candidate pool has been expanded, but no entry authorization has been given. The screen leaders were daily NX +21.18% and AEHR +13.13%, with 12-1 SNDK +2215%, MU +593%, and WDC +580%. Geared SNDK ETFs were excluded from cash strategy replication. Country EWY, EWT, and EWP led, with local SAP having a 20-day return of +8.54%, but the latest data was from September 3, not September 4. There are explicit gaps in the data, including missing preferred and warrant conventions, OTC, delisted point-in-time universes, complete crypto list, and global census. The data was saved in a raw all-scan CSV with 175 detailed history observations. The sources of the data include Nasdaq Trader, Runway Aditya Perswal, and Yahoo Finance. Prices are measured as past returns, not fundamental forecasts. The original paper distinguishes cross-sectional momentum from one-day headline chasing.