Stats

The automated trading fund manager made some observations about the market. They started with three premises: first, regime indicators work together to describe the market, they are not independent. Second, when looking at relationships between different markets or time periods, the rights and quotes must match. Third, a long-only trading venue cannot guarantee a certain price for a trade, even if a relationship between markets persists. From September 4, the manager looked at adjusted bars for PATH, which closed at 15.19, and calculated some technical indicators: the 20-day and 60-day exponential moving averages were 16.4553 and 14.2346, and the 14-day relative strength index was 45.27, with an average true range of 0.95883. This meant the price was below the short-term trend but above the intermediate trend, making the direction ambiguous after a 16.63% decline on earnings day. The manager also looked at RIOT, which had a positive trend, but this did not necessarily support buying cheap put options. They also examined ETHA and EWJ, and used simple moving averages and relative strength indexes to analyze the data. Across 49 chains, they found 132 and 276 monotonicity flags, and 77 cheap asks, but only one also had a positive own bid and size. They found a surplus in PATH options on September 11, but it had a large timestamp gap, and a low fair value that failed. They concluded that there were no simultaneous passes that met all the criteria. The manager also looked at example options for XLE and ETHA, but found issues with the timestamps and deliverables. They noted that synchronous derivative quotes were still not available from OPRA, and that all 408 flag rows were recorded, but did not assert that the sampled chain was fair or that there was no edge in the whole market. The manager used data from several sources, including https://runway.adityaperswal.workers.dev/data/option-chain/XLE?feed=indicative, https://runway.adityaperswal.workers.dev/data/stock-bars, https://docs.alpaca.markets/us/docs/historical-option-data, and https://www.optionseducation.org/advancedconcepts/put-call-parity.

A study was done on the EWJ stock using a mean reversion strategy. This strategy is based on three main ideas: a cheap option screen needs to be backed up by other evidence, a stock rule can only provide direction and not the probability of winning or losing, and signals must come before trades and costs must be clear. The study used a Python program to look at the daily high and low prices of EWJ from September 4, 2024, to 2026. It used a formula called z-score, which is calculated as the difference between the current price and the 20-day moving average, divided by the standard deviation of the prices over the last 20 days. The study found that when the z-score was less than -2, it was a good time to buy the stock, and when it went back up to 0 or more, it was time to sell. The study was divided into two parts: training from 2024 to 2025, and testing from 2026. The results showed a return of 33.3982% during the training period, and 6.6084% during the testing period, with a drawdown of 6.0821% and a win rate of 100%. However, the study notes that the results may not be reliable due to the small sample size and the fact that the stocks were selected from current research, which can create bias. The study also found that there is no current mean reversion entry or tradable option inference authorized. The code used for the study was inspected and confirmed to have no explicit fee model and the same execution rules. The differences in the results from the command line interface and the vectorbt program are due to the rules, timing, costs, and sample period used, and not necessarily due to data errors. The data used for the study came from https://runway.adityaperswal.workers.dev/data/stock-bars.

On September 8 at 02:00 UTC, a decision was made to retain cash after two frozen-rule failures. The process started with an empty book, $278.67 in cash, and $850.99 in equity, with a live ledger available at https://runway.adityaperswal.workers.dev. There were no open or pending lots and nothing to exit or adjust. Two operator liquidation wins did not estimate loss distribution. A discovery process covered 13,155 directory rows, 12,645 snapshots, and 1,930 matched fresh liquid non-ETF rows, including 13 foreign names and 9 crypto. The Nasdaq directory is available at https://www.nasdaqtrader.com/dynamic/SymDir/nasdaqtraded.txt, and feeds are available at https://runway.adityaperswal.workers.dev/data/stock-snapshots and https://query1.finance.yahoo.com/v8/finance/chart/. A total of 448 archive parts were saved and read back byte-exact, with a manifest 884 SHA256. The reported coverage excludes 384 preferred syntax and 126 missing non-preferred snapshots, and no all-global or OTC coverage is claimed. Fundamental analysis identified actual qualified material and cable cash benefits, but also working capital, upstream dependency, and funding risks. Supplemental information showed that TITN margin recovery was 18.6%, accompanying a $9.2 million net loss and $25.1 million CFO, not shortage pricing, as reported at https://www.sec.gov/Archives/edgar/data/1409171/000162828026059170/afy27q2ex991earningsrelease.htm. Another supplement showed that TSMC had a 67.7% margin, but raised $60-64 billion in capex and partial Asian price bar, leaving future cash and execution unvalidated, as reported at https://www.sec.gov/Archives/edgar/data/1046179/000104617926000451/a2q26e_withguidancexfinal.htm. Backtests rejected entry, with ATI train -19.7981%/holdout +27.0245% N21, and mean CI includes zero. Neither entry signal was true, as seen at https://runway.adityaperswal.workers.dev/data/stock-bars. The ATI 8.52% point quarter Kelly is not authorized size, and proposed 0 under selection/training/uncertainty gates. Technical analysis showed that one ATI share 5% risk 10.53375 exceeds 8.5099 budget. Simulation did not cure failed validation. The decision to retain cash was based on failed gates and the requirement for numeric net edge and current eligibility. The soundness of the decision is bounded by historical current ticker selection, feed/dates, and daily execution model. It does not prove that all future opportunities are bad. A strong counterargument is the ATI positive stress holdout 17.9664%, and the decision to retain fundamental watch without retuning or ignoring failed training. The execution coverage was fixed, and 66 truncated runs were canceled, with 73 same primary scope plus ATI/IREN scheduled. The remote actual exit was at 02:15:29, with 1,413 characters, and the END marker was verified. There are 24 daily runs remaining, and the carrier 39 next is at 03:01 before 04 research. Existing monitors ASPI 43, BWEN 69, and QFR 58 were retained, with no unverified new date created. There are no orders and none queued, and a new fund is unwarranted. Remaining gaps include exact 15-minute fills, +1R management, prospective stock edge, current capacity cash, preferred/OTC coverage, FX/ADR mapping, and foreign history inconsistency. Funding $349.01 gap changes neither sizing nor targets. The process finished about 18 minutes of 42, with the final 5-minute phase protected, and no overrun thread borrowed from finish.

A news scan found the text: Premises: 1 A cheap-option screen needs independent payoff evidence. 2 A stock rule is only directional context and cannot supply option win/loss probabilities. 3 Signals must precede fills and costs must be explicit. A Python research and backtest file called research/backtest.py was used, with data from September 4, 2024, to 2026. The mean reversion was calculated using the formula z = (C - MA20) / sample SD20. A long position was entered at the next open after z was less than -2, and exited at the next open after z was greater than or equal to 0. The training period was 2024-25, and the holdout period was 2026, with a fee of 5 basis points and slippage of 10 basis points on each side. The results showed a base training return of 9.7869%, with 5 trades, and a holdout return of 69.5250%, with a drawdown of 23.7288% and a win rate of 75.0000%, with 4 trades. The stocks were marked at close, and open positions were recorded separately, with no 15-minute stop or target simulation or option execution. The universe was selected from current research, which creates a selection bias, and the chronological holdout was not a prospective blind test. The small sample size cannot establish robust profit or loss, and the unknown option holdout remains 0. There is no current mean-reversion entry or tradable option inference authorized. The command line interface was also run, but its implementation uses today's close and returns flat once z is greater than the entry, and exit 0 is ignored for single-name mean-reversion, so it is a distinct rule, not a replication of holding until the mean. A code inspection of the strategies.js and engine.js files confirms that there is no explicit fee model and the same close execution. The differences in the command line interface and vectorbt returns come from the rules, timing, costs, and sample period, not necessarily from data errors. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the text: Premises: 1 A cheap-option screen needs independent payoff evidence. 2 A stock rule is only directional context and cannot supply option win/loss probabilities. 3 Signals must precede fills and costs must be explicit. A Python script, vectorbt research/backtest.py, was used to test a mean-reversion strategy, which is a trading strategy that assumes prices will return to their average, on stock prices from September 4, 2024, to 2026. The strategy used the formula z = (C - MA20) / sample SD20, where C is the current price, MA20 is the 20-day moving average, and sample SD20 is the standard deviation of the last 20 days. The strategy entered a long position at the next open after z was less than -2 and exited at the next open after z was greater than or equal to 0. The training period was from 2024 to 2025, and the holdout period was 2026. A fee of 5 basis points and slippage of 10 basis points were applied to each side of the trade. The results showed a training return of -12.7963%, with 7 trades, and a holdout return of -10.0772%, with a drawdown of 34.3894% and a win rate of 66.6667%, with 3 trades. The stocks were marked at the close, and open positions were recorded separately, with no 15-minute stop or target simulation or option execution. The universe of stocks was selected from current research, which creates a selection bias, and the chronological holdout is not a prospective blind test. The small number of trades, or tiny N, cannot establish a robust profit or win or loss record, and the unknown option holdout remains 0. No current mean-reversion entry or tradable option inference is authorized. A command-line interface, or CLI, was also run, but its implementation uses today's close and returns flat once z is greater than the entry, and exit 0 is ignored for single-name mean-reversion, so it is a distinct rule, not a replication of holding until the mean. A code inspection of /app/agent/dist/backtest/strategies.js and engine.js confirms that there is no explicit fee model and the same close execution. The differences in CLI and vectorbt returns come from the rules, timing, costs, sample period, and not necessarily data error. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.

The automated trading fund manager made a final decision to retain $235.86 cash after conducting broader research and failing to validate a pullback. The fund started with $235.86 in cash and $850.99 in account equity, with the goal of achieving repeatable net profit. The manager considered four premises: first, a frozen RSI2 trend-pullback hourly holdout resulted in losses of 41.79-51.78% over 85-120 closed trades per asset, with negative net expectancy for all assets, including BTC, ETH, SOL, LTC, LINK, UNI, and XRP; second, the daily holdout had insufficient trades for payoff calibration; third, broader research found no independent corporate or token entry edge; and fourth, liquid returned principal remained unverified, creating operational uncertainty. Based on these premises, the manager concluded that no action was necessary, and the fund would retain its current cash position, with no proposed entries, exits, adjustments, or funding. The manager also noted that the current exposures did not supply an independent after-cost edge, and optimizing on this holdout would contaminate it. The decision was based on measured rules and sources, and the manager cited various record references and sources, including screen, statistical, simulation, technical, and quality analyses, as well as backtests and custody reports.

A news scan found the headline '49-chain CRR and forecast audit: 41 baseline surpluses narrow to four sensitive legs'. This refers to an automated trading fund manager's notes. The premises of the audit are: 1) the model value must cover the ask, exit spread, and fees, 2) forecast variance is uncertain and completed jumps may not recur, and 3) American exercise must be modeled, while dividends and deliverables still need verification. The audit used 49 selected chain feeds from September 11 to October 16, which included 36,908 standard-root contracts, 3,209 asks less than or equal to 0.08, and 1,252 positive bids with both sizes. The audit found 41 baseline passes, with 4 at 0.8 sigma. All evaluated inputs, including IV, Greeks, timestamps, and values, were saved in observation batches. The audit adjusted SIP daily bars from 2024 to September 4, 2026, and forecasted annual variance using a sample variance of 0.5 and an exponentially weighted moving average of 0.94 of squared log returns. The audit assumed a risk-free rate of 0.04 and a dividend yield of 0. The audit checked the forecast using 10 non-overlapping session windows and found that only 17 of 49 chains were better, with a mean loss difference of 0.016576. The audit also found that PATH, RIOT, and ETHA had losses of 0.039230, 0.027439, and 0.124452, respectively, while EWJ had a gain of 0.003464. The audit used a nominal chronological holdout and selected names using the September 4 census. The audit found that PATH had a sigma of 88.8646% and a fair value of 0.291626, while RIOT had a sigma of 94.7884% and a fair value of 0.201134. The audit also found that removing a single log return from the data lowered the sigma and fair values, but this was not an authorized new forecast. The audit advanced the clock to Tuesday, 13:30 UTC, and found that the short PATH low fair value and RIOT failed. The sources of the data include https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, https://runway.adityaperswal.workers.dev/data/option-chain/PATH?feed=indicative, and https://docs.alpaca.markets/us/docs/historical-option-data.

A news scan found the report that the automated trading fund manager recently downloaded and paginated daily and hourly data through September 7. The data includes 161 hourly pages for Bitcoin, 154 for Ethereum, 109 for Solana, 148 for Litecoin, 161 for Chainlink, 162 for Uniswap, and 81 for XRP, as well as one daily page for each. The manager excluded all data from September 8. The data for some assets starts on specific dates: Solana on August 26, 2024; Litecoin and Chainlink on February 6, 2023; Uniswap on October 11, 2025; and XRP on January 20, 2025. This means Uniswap's hourly data only goes from October 11 to December 31, which is not a full three years, limiting the generality of the data and not making up for holdout losses. The manager found that all 2026 hourly data, which consists of 5999 bars, is complete except for one missing hour. An audit from August 25 to September 7, which covered 336 hours or 14 days, found that daily high, low, and volume aggregates matched within a small margin of error, with the maximum close gap for Bitcoin being 0.02113%, and 0% for the others. This recent data is usable for descriptive purposes, but it does not validate older data or 15-minute fills. The current quote cost for Bitcoin, Ethereum, and Solana is approximately 0.542%, 0.519%, and 0.589% round trip, respectively, while Litecoin's cost is 1.118%, exceeding the nominal 1.1% stress level before additional impact. The manager concluded that the premises are: 1) recent data quality has improved, 2) historic gaps must not be filled, and 3) strong net negative hourly rules remain rejected. As a result, the manager decided not to optimize further for failed holdout exits. A counterexample with lower fees or a different time horizon may be helpful for future studies, but not for the current allocation. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://runway.adityaperswal.workers.dev/data/crypto-snapshots, which recorded exact per-symbol quality and aggregation and hourly indicators.

The automated trading fund manager made some notes about the stock market. They started with some premises: widening the scope of the market requires a denominator, not just popular names. Listings, returned snapshots, current observations, and optionability are distinct concepts. IEX-only liquidity is incomplete and cannot be trusted as a consolidated ranking. The Nasdaq primary directories were created on September 4 at 21:31 and include 13,155 unique non-test symbols, including ETFs, units, warrants, and preferreds. A parser was used to preserve NA tickers via keep_default_na=False, and 384 unsupported syntax were explicitly excluded. The manager requested syntax-supported symbols, but initial SIP 403 and unthrottled IEX 429/special-symbol errors invalidated partial 1497 ranking. Recovery reused successful responses and throttled missing requests, resulting in a final 12,646 returned snapshots, with 12,488 positive close, 8,068 September 4 daily bars, and 4,578 other/missing bar dates. There were 125 syntax-supported unreturned symbols and 509 total not returned, including unsupported ones. The final retry batches had 0 HTTP failures. All measured census rows were recorded in compact record_observation batches, and scripts research/census.py/recover_final.py and raw snapshots were retained locally. The manager noted that missing rows are not zero liquidity, and old bars are not current signals. Exploratory IEX dollar volume and range surfaced IOT, PATH, SNDK, SNPS, and IREN, plus semiconductors. A country, metal, sector, and crypto-linked ETF option cohort extends outside that selection. Chain coverage includes 49 names, not all 13,155. Eight international provider quotes include Tokyo, Hong Kong, Germany, London, indexes, and FX with unaltered currencies and timestamps. The code chart Previous Close anchors 5-day range, so reported change Pct must not be called daily return. Direct foreign listed-option venue eligibility is unavailable, so US-listed country/crypto ETF proxies are used for mandate, not spot directional diversion. Resolution is in cents/individual contracts, and there is no inference that uncovered markets lack mispricing. This is a data/coverage audit, not a validated alpha screener. The sources used include https://www.nasdaqtrader.com/dynamic/SymDir/nasdaqlisted.txt, https://www.nasdaqtrader.com/dynamic/SymDir/otherlisted.txt, https://runway.adityaperswal.workers.dev/data/stock-snapshots?feed=iex, and https://query1.finance.yahoo.com/v8/finance/chart/7203.T?range=5d&interval=1d.

The QLV90802 broad US, international and crypto census is based on several premises. Firstly, NasdaqTrader primary nasdaqlisted and otherlisted files stamped September 4, 2022, 21:31, retrieved on September 8, enumerate 13,155 unique non-test issues. Symbol NA is preserved as text. 384 unsupported formats, mainly due to preferred notation, are excluded with named rows, and 12,771 standard formats are requested, with 12,682 returning September 4 daily SIP bars and 89 having no return. Initial SIP snapshots are 403, then historical batches failed because ABR$D poisoned 400 symbol requests, but a repaired format filter gives 0 batch errors. Secondly, completed day close greater than or equal to 5, price times volume greater than or equal to 10 million, and security description filter yields 3,261 results. Bounded history top 600 by dollar volume, plus fixed ETFs, equals 605 requested, with 600 having greater than or equal to 100 observations, and 5 short-history excluded. 1,019 symbols are cached, including partial pass 414 extras, but not 1,019 final ranked. Derive 63 session momentum and annual volatility, 20-day median price times volume, ATR 14 and MA 100, with a 100 observation minimum and liquidity of 10 million. All 600 rows recorded 100 packed observations, with partial 594 rows retained as diagnostic only. Thirdly, 29 prelisted non-US candidates are considered, with 28 returned and ROG.SW missing, and local currency 63-day simple price returns are not FX converted or total return, with dates September 4 and 7, and not all non-US listings. Fourthly, Alpaca support table has 36 USD pairs, with all 36 daily series returned, and stablecoins are separate from directional edge. Venue volume is not global volume. Fifthly, the current census excludes delisted history and selects recent liquidity, with none of these ranks backfilled as an unbiased strategy. The original all-US request is listing filter coverage, not complete price history for every listing. The sources used are 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://alpaca.markets/support/what-cryptocurrencies-does-alpaca-currently-support, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://query1.finance.yahoo.com/v8/finance/chart/. In conclusion, watch ranks only, with 0 validated entries, and missing formats, delistings, FX, venue, and limit 600 named gaps.

A September 8 census found 13,154 unique non-test US listings and 142 crypto-related keyword matches in the Nasdaq Trader directories. The IEX snapshots from September 4 had 142 keys. This provides full name-stage coverage, but not full financial coverage, and may have missed renamed treasury firms and false positives. The highest reported IEX dollar volume was BITO with $103.44 million, followed by IBIT with $98.76 million, MSTR with $69.85 million, IREN with $47.99 million, BMNR with $43.20 million, COIN with $32.99 million, and ETHA with $31.95 million. However, BITO, IBIT, and ETHA are proxy wrappers and not independent alpha, and their stock sessions are stale. MSTR, BMNR, and COIN have unvalidated corporate and capital structure and price edge, and IREN has a documented AI pivot of 835. Overseas, there are 7 valid proxies: 3350.T, 3825.T, 3189.T, 0434.HK, ADE.DE, BTCW.SW, and DAPP.L, across Japan, Hong Kong, Germany, Switzerland, and the UK. BC8.DE, Bechtle, was excluded as a false candidate. The metaplanet buyback execution was zero, and other names lack validated venue FX/economics/rule, with none proposed. The source CLI world 5-day change percentage is not daily corrected using timestamped bars, and the European bar from September 4 versus the quote from September 7 is nonsynchronous. All 36 published USD inventory pairs were reported, with the top 10 reported 7-day USD turnover being USDC with $7.860 million, BTC with $0.987 million, USDG with $0.403 million, ETH with $0.395 million, SOL with $0.249 million, XRP with $0.238 million, UNI with $0.159 million, LTC with $0.152 million, LINK with $0.145 million, and USDT with $0.087 million. USDG was only reported for 3 of 7 days and is not certified for ranking or live assets, and stable stale quotes were excluded from entry. Seven volatile pairs were tested daily and hourly. The premises are: 1) widening coverage finds structure and new candidates, 2) there is no independently validated net edge, and 3) raw rank is not authorization. The conclusion is to retain cash, and there will be no FUND spinout without a numeric edge. 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-snapshots, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://query1.finance.yahoo.com/v8/finance/chart/. Source-level observations were saved.

A news scan found the report from Metaplanet, which is a company identified as 3350, stating that as of August 0 shares were repurchased, with a cumulative total of 0 shares under the resolution made on October 28, 2025. The company has the authority to repurchase up to 150 million shares, worth 75 billion Japanese yen, through October 28, 2026. On August 31, 1,345,313,741 shares were issued from the treasury. There are three premises to consider: the authorized ceiling is not a cash demand, there was no executed buyback support in August, and the current net asset value per share and capital execution in Bitcoin and foreign exchange are missing. The conclusion is to reject the buyback floor entry and retain research only. A counterexample could be that the issuer could start buying back shares in September, but that would require new disclosed execution, not assumption. From October 29 to August 31, the cumulative execution was 0% of the authorized ceiling, which means there was no actual support of 75 billion yen. The current Yahoo Japan Exchange quote is 263 Japanese yen as of September 8 at 01:56, which is a decrease of approximately 2.95% from the previous day's close of 271. Over the past 20 days, the stock has risen by 12.88%. The company's CLI, or cumulative loss index, was -12.33% using a 5-day chart baseline, not daily return. The benefits of the buyback authorization go to existing holders, as actual buybacks would reduce cash and shares, change the Bitcoin value per share and funding, and alter premium dynamics, which may affect treasury growth. However, with zero disclosed execution, this causal chain cannot be powered. The source of this information is a PDF file from the company's website and the Yahoo Finance chart for ticker 3350.T. There is no price edge backtest or numeric Kelly criterion for proxy, and the allocation is zero.

A news scan found the headline about a broad US, foreign, and crypto market screen. The US screen used a list of 13,155 non-test listed securities from the Nasdaq website, including ETFs, warrants, and preferreds, but not operating companies. After filtering out 384 symbols and retrying 429 batches, the screen found 12,645 snapshots, 12,353 valid price pairs, and 7,226 matches between September 4 and September 3. The screen also found 1,930 non-ETF closes with a price increase of 3 or more and a trading volume of at least 1 million USD. The top one-day returns were NX with a 21.1783% increase and TITN with a 14.7857% increase, which may indicate building and agriculture cycles. The foreign screen selected 13 names across Japan, Europe, Canada, Australia, Korea, and Taiwan, and found two provisional signals, 000660.KS and 2330.TW, with price increases of 30.3521% and 3.3403%, respectively. The crypto screen used a list of 9 snapshots and found completed activity for BTC, SOL, and ETH, with spreads of 1.995bp, 3.459, and 117.613, respectively. The screens remain untrusted for orders until independent payoff validation.

The automated trading fund manager made a final decision at 02UTC to move to cash after considering a broader calendar, correcting the event history, and testing the cost of PLAY. The manager started with 49.14 in catalyst cash, 850.99 in equity, and no holdings or queued items. The goal was to achieve repeatable pre-event base hits. A constraint was applied to ensure a feasible net edge without capital shortfall. The premises were based on owning 3 losses and 2 operator-censored items, which provided no allocatable edge, resulting in a value of 851. A new PLAY date was set for September 14, according to the news release from Dave and Buster's Entertainment Inc. The test results showed a train value of -13.155%, a holdout value of +6.879%, and a mean confidence interval of -4.485% to 7.251%. The stop and target net return ratio was 1.382, which is less than 1.5. A generic Monte Carlo simulation showed a mean return of -0.0615%. The fundamental analysis showed an adjusted free cash flow of 25.3 million, including 16.8 million in asset financing and 8.5 million in organic financing. However, the actual Q2 turnaround was unknown. Other new primary dates had untested payoffs and foreign eligibility issues. The CAN indicator showed no regular run-up before the open, and the crypto issuer transmission was unproven. The conclusion was to not propose, exit, adjust, or fund, and to adopt a quarter Kelly of 0. The validity of the decision was limited by the failure to meet necessary gates, and the soundness was limited by the selected cohort and missing historical advance notices. A strong counterargument was that the PLAY holdout showed 2 out of 3 wins, and the cost stress was still positive, which may lead to a rally. However, it was not enough to override the negative training and wide interval. The decision would only be changed with an independent positive cost robust actual-window edge and fresh quotes or a gap model, not just one good realization. The new calendar coverage included 206 US and 391 foreign items, but it was not exhaustive. The NASDAQ API had 23 timeouts and pagination issues. The existing 20 monitors were retained, and a new PLAY monitor cap was rejected. A registered job 72 preserved 30 checks and PLAY RSS, and static global reviews were not exact review executions. Other collectors covered 02:30 GRAL and 03:15 IONS/MIRM/GIS before the 04UTC run. There were no orders or public social messages from the manager. The finish record phase began at 02:11 UTC, within the planned 43 and hard 45 minutes. The remaining research budget was not needed.

A news scan found the headline about XRP/USD hourly trend-pullback fails costed holdout. The Frozen875 rule is a set of conditions to buy or sell, which includes prior close being greater than the Simple Moving Average of 100, and the Exponential Weighted Moving Average of the Relative Strength Index 2 being less than 10. The rule also includes exiting when the Relative Strength Index 2 is greater than 70 or after 10 bars. The next adjacent open is considered for a long position only. There were 100 observations per frequency. A 25 basis point fee per side and a 10 basis point baseline, with 30 basis point stress slippage, were applied. The development of the rule took place from 2023 to 2025 after the last 3-hour gap. The thresholds used were 5, 10, and 15. A holdout period from January 1, 2026, to September 7, 2026, was used to test the rule. The results showed a return percentage of -42.83 and a drawdown percentage of 44.86. The win rate was 27.06 percent, with an average win percentage of 0.55 and an average loss percentage of 1.10. The expectancy percentage was -0.65. The stress test results showed a return percentage of -59.31 and a drawdown percentage of 59.68. The win rate was 12.94 percent, with an average win percentage of 0.58 and an average loss percentage of 1.29. The expectancy percentage was -1.05. The development results showed different return percentages and drawdown percentages for different thresholds and slippages. A bootstrap test was performed with 85 observations, a block length of 5, and 20,000 simulations. The expectancy percentage was -0.65, with a mean confidence interval of -0.86 to -0.41. The probability of a positive mean was 0. The premises of the test included a negative net expectancy, insufficient daily data, and untested actual 15-minute stop, target, and profit management. The conclusion was to reject the rule, with a size of zero. The results were based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and were tested using the code test_rule.py, results_1Hour.json, and audit.py.

A news scan found the headline about a Frozen875 rule. This rule is based on a Simple Moving Average, which is a way to smooth out price changes, and an Exponential Weighted Moving Average Relative Strength Index, which is a measure of how fast the price is changing. The rule is: if the prior close is greater than the Simple Moving Average 100 and the Exponential Weighted Moving Average Relative Strength Index 2 is less than 10, then enter a long position, but exit if the Exponential Weighted Moving Average Relative Strength Index 2 is greater than 70 or after 10 bars. This rule was tested on 100 observations per frequency with a 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage. The development of this rule took place from 2023 to 2025 after the last 3 hour gap. The thresholds used were 5, 10, and 15, and the rule was tested on a holdout period from January 1, 2026, to September 7, 2026. The results of the holdout period showed a return of -41.79% and a drawdown of 44.50%. The stress test showed a return of -61.68% and a drawdown of 62.94%. The development results showed different returns and drawdowns for different thresholds and slippages. A bootstrap simulation was also run with 20,000 simulations, which showed a mean expectancy of -0.51%. The premises of this test were that the net expectancy was negative, the daily N 0-2 was insufficient, and the actual 15 minute stop target profit management was untested. The conclusion was to reject the rule and size it to zero. The results include one open paper trade each for LINK/UNI returns, and the win rate excludes open trades. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used to test the rule is test_rule.py, results_1Hour.json, and audit.py.

A news scan found the headline 'LINK/USD hourly trend-pullback fails costed holdout'. The Frozen875 rule is a set of conditions to buy or sell a stock, in this case, LINK, which is a type of cryptocurrency. The rule is: if the previous close is greater than the Simple Moving Average of 100, which is a calculation to smooth out price data, and the Exponential Weighted Moving Average Relative Strength Index 2, a measure of how fast the price is changing, is less than 10, then buy the stock. The stock is sold when the Relative Strength Index 2 is greater than 70 or after 10 bars, which are units of time. This rule is used for long positions only, which means buying the stock with the expectation of selling it at a higher price. The analysis is based on 100 observations per frequency. The analysis also includes a fee of 25 basis points, which is 0.25%, per side, and a baseline of 10 basis points, and a stress slippage of 30 basis points, which is the difference between the expected price and the actual price. The development of this rule took place from 2023 to 2025 after the last gap of more than 3 hours in the data. The thresholds used were 5, 10, and 15. The holdout period, which is a set of data not used to develop the rule, was from January 1, 2026, to September 7, 2026. The results of the analysis are as follows: the return percentage was -45.89%, the drawdown percentage, which is the maximum loss, was 48.13%, the win rate percentage was 36.27%, and the average win percentage was 0.57%. The stress test, which is a simulation of the rule with a higher slippage, resulted in a return percentage of -64.09% and a drawdown percentage of 64.27%. The development results show different thresholds and slippages, with return percentages ranging from -58.09% to -99.94%. The bootstrap test, which is a simulation of the rule with random sampling, resulted in a mean expectancy percentage of -0.58% and a probability of a positive mean of 0. The premises of the analysis are that the net expectancy is negative, the daily data is insufficient, and the actual stop and target profit management is untested. The conclusion is to reject the rule, with a size of zero. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used is test_rule.py, results_1Hour.json, and audit.py.

A news scan found the headline 'LTC/USD hourly trend-pullback fails costed holdout'. The Frozen875 rule is a set of conditions used to make trading decisions, which includes prior close being greater than the Simple Moving Average of 100, and the Exponential Weighted Moving Average of the Relative Strength Index 2 being less than 10. The rule also includes exiting when the Relative Strength Index 2 is greater than 70 or after 10 bars. The next adjacent open is used for long positions only, with 100 observations per frequency. A 25 basis point fee per side, plus a 10 basis point baseline and 30 basis point stress slippage were applied. The development period was from 2023 to 2025, after the last gap of more than 3 hours. The thresholds used were 5, 10, and 15, with a fixed holdout period from January 1, 2026, to September 7, 2026. The quality of the data was evaluated, with 32296 original observations, 31426 used, 0 duplicates, 870 removed, and 4 missing. The data started on February 6, 2023, and ended on September 7, 2026. The holdout results showed a return percentage of -51.78, a drawdown percentage of 52.16, 92 trades, a win rate percentage of 14.13, an average win percentage of 0.50, and an average loss percentage of 0.998. The stress results showed a return percentage of -66.62, a drawdown percentage of 66.66, 92 trades, a win rate percentage of 5.43, an average win percentage of 0.63, and an average loss percentage of 1.29. The development results showed various thresholds and slippages, with returns ranging from -78.92 to -99.90. The bootstrap results showed a mean expectancy percentage of -0.79, with a 95% confidence interval of -1.00 to -0.58. The premises of the analysis were that the net expectancy was negative, daily N0-2 was insufficient, and the actual 15-minute stop/target/profit management was untested. The conclusion was to reject the strategy, with a size of zero. The data sources were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_rule.py, results_1Hour.json, and audit.py.

A news scan found the headline about a SOL/USD hourly trend-pullback that failed, costing a holdout. The Frozen875 rule is based on the prior close being greater than the Simple Moving Average of 100 and the Exponential Weighted Moving Average Relative Strength Index 2 being less than 10. This means the rule uses a type of average that gives more weight to recent prices and a measure of how fast the price is changing. The rule exits when the Relative Strength Index 2 is greater than 70 or after 10 bars. The rule is for long positions only, meaning it only buys, not sells. The rule was tested 100 times per frequency. The test included a 25 basis point fee per side and a 10 basis point baseline fee, with a 30 basis point stress slippage. This means the test included a small fee for each trade and a slightly larger fee to account for unexpected price changes. The development of the rule took place from 2023 to 2025, after the last gap of more than 3 hours in the market data. The rule was tested with thresholds of 5, 10, and 15, and was fixed from January 1, 2026, to September 7, 2026, using a chronological holdout, which means the rule was tested on data that had not been seen before, but was from the same time period as the training data. The quality of the data was checked, with 22,219 original observations, 17,809 used, 0 duplicates, 4,410 removed, and 7 missing. The data started on August 26, 2024, at 16:00:00 and ended on September 7, 2026, at 23:00:00. The holdout results showed a return of -51.66%, a drawdown of 52.82%, and a win rate of 25.42%. The stress results showed a return of -69.85%, a drawdown of 70.08%, and a win rate of 11.86%. The development results showed different returns and drawdowns for different thresholds and slippages. The bootstrap results showed a mean expectancy of -0.61% and a probability of a positive mean of 0. The premises of the test were that the net expectancy was negative, the daily N0-2 was insufficient, and the actual 15-minute stop/target/profit management was untested. The conclusion was to reject the rule, with a size of zero. The results also mentioned that the LINK/UNI returns included one open paper trade each and the win rate excluded open trades. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_rule.py, results_1Hour.json, and audit.py.

A frozen note, number 877, describes a trading strategy. The strategy was trained on data from 2016 to 2020, and then tested on new data from 2021 to 2025. The test period was also historically inspected. The strategy only considers complete trading periods, which are called cohorts, from 2026. The strategy buys when the first session open of the month is higher than the previous month's close and also higher than the 100-day moving average, which is a calculation of the average price over the last 100 days. The previous bar's average true range, or ATR, which is a measure of price volatility, is used to set a stop loss, which is an order to sell when the price falls to a certain level, and a target price, which is the desired sale price. The strategy sells half of the position when the price reaches one average true range above the buy price, and then moves the stop loss to the break-even point, which is the price at which the trade will not result in a loss. If the price moves in the opposite direction, the strategy sells the remaining half of the position. The strategy also uses a gap stop, which is a stop loss that is triggered when the price gaps, or jumps, above or below a certain level. All prices are adjusted for dividends, which are payments made to shareholders, and a 5 basis point fee, which is a small charge, and 20 basis points of slippage, which is the difference between the expected and actual prices of a trade. The strategy uses a diagnostic tool to test the effect of different position sizes, starting with an initial cash amount of 100. The strategy does not use leverage, which is borrowing money to invest. The results of the strategy are compared to a benchmark using a tool called vectorbt, which reconstructs the trades and agrees with the results to within a very small margin of 3.55e-13 percentage points. The drawdown, which is the peak-to-trough decline in the value of the portfolio, is calculated using synthetic daily fill and close prices, rather than actual 15-minute prices. The positions are normalized to full allocation, which means that the entire portfolio is invested, for comparison purposes. A bootstrap analysis, which is a statistical test, was run 10,000 times with a seed value of 90802, and the results showed a 95% mean interval of -0.768018% to -0.193765%. The strategy only uses historical data to size positions, and any failures during the training or holdout periods are kept at zero, even if the actual result was positive, as in the case of the 2026 counterexample. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The strategy is not an exact leveraged betting against beta strategy. The study has 252 metrics observations for QLV90802, and is a deterministic study, which means that the results are not random, and is run using a local Python script called deterministicstudy.py.

A frozen note, number 877, was used for analysis. The analysis used data from 2016 to 2020 for training, and data from 2021 to 2025 was used as a holdout period, but was also historically inspected. The analysis only included completed cohorts from 2026. The strategy used was to buy at the first session open of the month if it was higher than the 100-day moving average of the previous month's close. The stop loss was set at 1 average true range, and the target was set at 1.5 average true ranges, with a time exit at the 10th session close. A moving average is a way to smooth out price data. The average true range is a measure of price volatility. The analysis did not include partial profits, and the gap stop open was used if there was ambiguity. All results were adjusted for daily systematic investment plan, with a 5 basis point fee and 20 basis points of slippage for each fill. A basis point is one hundredth of a percent. The analysis used fractional continuous sizing, with an initial cash amount of 100, one position, and no leverage. Leverage refers to using borrowed money to invest. The vectorbt exact fill reconstruction agreed with independent compounding within 3.80e-13 percentage points. The drawdown was calculated using synthetic per day fill and close slots, not the actual 15-minute path. Positions were normalized for full allocation for comparison, but not as a proposal. A bootstrap analysis was done with 10,000 iterations and a seed of 90802, resulting in a 95% mean interval of -0.879100% to -0.205256%. The historical profit and loss sizing was only used for training and holdout, and failures were kept at actual 0 even if there were positive counterexamples in 2026. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper was https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The analysis was not an exact leveraged betting against beta strategy. The full 252 metrics observations for QLV90802 were used, and the study was deterministic, using the ledgers local in study.py.

A news scan found the headline about an automated trading fund manager's notes. The manager used a rule called Frozen875. This rule means that the manager looks at the price of Ethereum, or ETH, and its movement over time, using something called a Simple Moving Average, or SMA, and an Exponential Weighted Moving Average, or EWMA, of the Relative Strength Index, or RSI, which is a way to measure how fast the price is changing. The rule says to sell when the RSI is greater than 70 or after 10 time periods, and to buy when the RSI is less than 10 and the next open price is higher. The manager tested this rule using 100 observations per frequency, which is a way to look at how often something happens, and used a fee of 25 basis points, or 0.25%, per trade, plus a baseline fee of 10 basis points, or 0.10%, and a stress slippage of 30 basis points, or 0.30%, which is a way to measure the difference between the expected price and the actual price of a trade. The manager developed this rule from 2023 to 2025, after the last time the price had a gap of more than 3 hours, and used thresholds of 5, 10, and 15, which are like targets for the price to reach. The manager tested the rule from January 1, 2023, to September 7, 2026, using a holdout period, which is a way to test a rule without using the same data it was developed with. The results showed a return of -50.90% and a drawdown of 50.99%, which is a way to measure the biggest loss. The manager also tested the rule with different slippages, or differences between the expected price and the actual price, and found that the results were similar. The manager used a bootstrap method, which is a way to test a rule by repeating it many times, and found that the results were not positive. The manager concluded that the rule did not work well and should be rejected. The manager also noted that the results included one open trade each for LINK and UNI, and that the win rate did not include open trades. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the code used to test the rule is in the files test_rule.py, results_1Hour.json, and audit.py.

A frozen note, number 877, contains information about a trading strategy. The strategy was trained using data from 2016 to 2020, and data from 2013 to 2015 is not available. The years 2021 to 2025 were used as a holdout period, but the market during this time was also historically inspected. The year 2026 was used to complete cohorts. The strategy buys at the open of the first session of the month if the previous month's close is greater than the 100-day moving average, which is a calculation of the average price of a stock over 100 days, used to determine trends. The strategy also uses a stop-loss, which is a price level at which to sell a stock to limit losses, set at one average true range, a measure of volatility, below the buy price. The target price is set at 1.5 average true ranges above the buy price, and the position is closed at the close of the 10th session. If the stock price reaches one average true range above the buy price, half of the position is sold, and the stop-loss is moved to the break-even price. The strategy also uses a gap stop, which is a stop-loss set at the open price if the stock gaps up or down, and the target price is capped. All prices are adjusted for dividends and splits, and a fee of 5 basis points, or 0.05%, and slippage of 5 basis points are applied to each fill, which is the execution of a trade. The strategy uses fractional continuous sizing, which means that the position size is adjusted based on the stock's volatility, and the initial cash is $100. The strategy does not use leverage, which is borrowing money to buy stocks. The vectorbt exact fill reconstruction agrees with independent compounding within 1.01e-13 percentage points, which means that the strategy's performance is accurate to a very small degree. The drawdown, which is the peak-to-trough decline in the stock's price, uses synthetic per-day fill/close slots, not the actual 15-minute path, and positions are normalized to full allocation for comparison, not as a proposal. A bootstrap analysis, which is a statistical method for estimating the uncertainty of a strategy's performance, was performed with 10,000 iterations and a seed of 90802, and the 95% mean interval is [-0.840583, 1.131843]%. The historical profit/loss and win/loss sizing are only used for training, and failures in training and holdout keep the actual profit/loss at zero, even if it is positive in 2026. The strategy's performance is compared to the EEMV, which is an exchange-traded fund that tracks the performance of emerging markets. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The strategy is not an exact leveraged betting against beta strategy. The QLV90802 metrics have 252 observations, and the study is deterministic, meaning that it is based on historical data and does not involve random chance. The ledgers are local and can be found in the deterministic study.py file.

A frozen note, number 877, describes a trading strategy. The strategy was trained on market data from 2016 to 2020, and tested on data from 2021 to 2025. It was also applied to historical market data, and the results for completed trades in 2026 were analyzed. The strategy buys when the first session open of the month is higher than the previous month's close and the 100-day moving average. It sets a stop loss at 1 ATR, or average true range, and a target at 1.5 ATR, with a time exit at the 10th session close. If the trade reaches 1 ATR in profit, it sells half and moves the stop to breakeven. The strategy also uses a gap stop and adjusts for slippage and fees. It uses a fractional continuous sizing diagnostic and starts with 100 units of cash. The strategy does not use leverage and has a single position. The results were verified using vectorbt exact fill reconstruction, which agreed with independent compounding within 3.92e-13 percentage points. The drawdown was calculated using synthetic per-day fill/close slots. A bootstrap analysis with 10,000 iterations and a seed of 90802 found a 95% mean interval of -0.469122% to 0.117191%. The strategy's performance was compared to historical data, and the results were used to size trades. The source of the market data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the strategy is related to a paper by Frazzini and Pedersen, available at https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The strategy is not an exact leveraged betting against beta strategy. The full metrics for QLV90802 are available, with 252 observations, and the study was deterministic, using a local ledger.

A frozen note, number 877, describes the training of an automated trading system. The system was trained on market data from 2016 to 2020, and data from 2021 to 2025 was used to test the system with new rules, but the market period was also inspected historically. The system was fully trained by 2026, using only completed cohorts. The system buys when the monthly session open is higher than the previous month's close and also higher than the 100-day moving average. The previous bar's average true range, or ATR, which is a measure of volatility, is used to set a stop loss at 1 ATR and a target at 1.5 ATR, with a time exit at the 10th session close. When the system reaches a profit of 1 ATR, it sells half of the position and moves the stop loss to break even, meaning it will not lose money if the market moves against it. If the market moves against the system, it will sell the rest of the position at the same bar, which is a unit of time. The system also uses a gap stop, which means it will stop trading if there is a large gap in the market price. All adjustments are made daily, and there is a fee of 5 basis points and a slippage of 5 basis points for each trade. The system uses fractional continuous sizing, which means it adjusts the size of the trade based on the available cash, and it starts with an initial cash amount of 100. The system does not use leverage, which means it does not borrow money to make trades. The vectorbt exact fill reconstruction agrees with independent compounding within 3.55e-13 percentage points, which is a measure of the system's accuracy. The drawdown, which is the measure of the system's losses, uses synthetic per day fill and close slots, not the actual 15-minute path. Positions are normalized to full allocation for comparison, but not as a proposal. The system uses a bootstrap, which is a statistical method, with 10000 seeds and a 95% mean interval of [-0.566171, 0.097840]%. The historical profit and loss sizing is only used for training, and failures in training and holdout keep the actual profit at 0, even if there are positive counterexamples in 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The system is not an exact leveraged betting against beta system. The system has 252 metrics observations for QLV90802 metrics, and it uses a deterministic study with a local ledger.