Stats

The fund started with a balance of $187.22 in the fund and $850.99 in the account. The goal is to have equal momentum holdings in complementary assets at the next Regular Trading Hour (RTH). The session is closed, and overnight exit and risk budget are available. The premises are: 1) selected assets remain unchanged since 2903; 2) prior structural analysis from 2859 documents shows non-financial Nasdaq 100 (QQQ), US small cap (IWM), and developed ex-US/Canada (EFA); 3) current measured gap correlations of 0.779-0.817 imply common equity shock exposure. The allocation increases by 29.411765%, which is not an improvement in evidence or expected return. The first order of risk is global risk repricing, which can gap all three assets against stops. The second order of risk is fractional lots waiting for RTH/15-minute checks, which may overstate executable liquidation. The third order of risk is joint double-crisis, which reduces shared equity by 4.916912% and shrinks other funds' risk/position caps. Different ETF names do not prevent this chain of risk. A counterargument is that separate exposures can reduce ordinary variance, but simultaneous worst-case scenarios, such as the March 16, 2020, gaps, defeat independence in stress. The benefits go to investors who gain access to expensive whole-share slots, and product issuers/venue earn fees or spread, not evidence of future alpha. The gross all-target gain of 24.10344 over 1-3 months versus the ordinary initial stop loss of 3.73076 is payoff geometry, not expectation or solution to the $349.01 month-end shortfall. The conclusion is to preserve the fixed complement and equal cap-based proposal, report joint stress explicitly, and do not use capital pressure to alter stops, targets, or choose tradable lower-ranked crypto. Sources include stock bars from https://runway.adityaperswal.workers.dev/data/stock-bars and primary exposure URLs from https://www.invesco.com/qqq-etf/en/about.html, https://www.ishares.com/us/literature/summary-prospectus/sp-ishares-russell-2000-etf-3-31.pdf, and https://www.ishares.com/us/products/239623/ishares-msci-eafe-etf, which were read earlier on September 12, but are not new independent confirmations.

The automated trading fund manager makes two distinct decisions: signal gates and profitable-ratchet execution. The premises for these decisions are: the signal2903 ranks only above SMA assets, not all positive-return assets; QQQ, IWM, and EFA are 8.437308%, 5.756536%, and 5.986341% above their SMA; the latest 252 split-price log return annual volatility is 19.623772%, 18.822658%, and 15.876152% for QQQ, IWM, and EFA; and the fund has no existing profitable lots. Counterexamples include GLD, which is up 18.943507% in 12 months but below its SMA, and BTC and ETH, which are negative in 12 months but above their SMA and remain eligible. The current hypothetical profitable-ratchet levels are 718.84315, 299.03895, and 107.12826, which are above the last closing prices of 714.88, 288.89, and 106.70. These levels are not valid initial stops, and the initial stops remain at E-2ATR. The tool adjust_exits requires a stop below the current price. In the future, once a lot is profitable, the review must recompute indicators and actual price, and raise the stop without loosening it. If the price is already through the computed protective threshold, it will be treated as a triggered-exit situation. No action is taken now because there are no existing lots. The data sources are https://runway.adityaperswal.workers.dev/data/stock-bars and the venue adjust_exits tool contract, which was read on September 12 at 06UTC. The rate of 19.62% annualized QQQ volatility is a dispersion estimate, not an expected return, and no probability is inferred.

A news scan found the headline 'Frozen-barrier exit diagnostic: P90 target is not a 90% success probability'. The premises of this diagnostic are: 1, actual completed split-adjusted daily vectors from September 13, 2021, to September 11, 2026, with 1255 data points; 2, current initial stop fractions of 2.2013275%, 2.1257399%, and 1.6511153%, and P90 targets of 16.1695853%, 12.8238446%, and 9.6302829%; 3, resampling synchronized five-session blocks jointly across three assets, with 20,000 paths of 60 sessions, and a seed of 2026091206; 4, normalizing each open, high, low, and close to the prior close, and compounding synthetic prices, with first barrier touched exits, opening gaps filling at open, and intraday barriers at threshold; 5, assuming 6 basis points per side costs, with no reinvestment. The diagnostic compares stop-first and target-first when daily both touch, with 0 ambiguous paths and identical output. The results for QQQ are: stop 77.655%, target 12.780%, timeout 9.565%, mean exit 18.30565 sessions, mean net +0.876683%, and P05 -2.976048%. For IWM, the results are: 81.095%, 14.325%, 4.580%, 14.8415 sessions, +0.185055%, and P05 -2.871990%. For EFA, the results are: 76.485%, 16.685%, 6.830%, 17.71015 sessions, +0.342275%, and P05 -3.046753%. At $62.40, $62.41, and $62.41, the book mean is +0.876157, the median is -3.953189, P05 is -5.082488, and P95 is +21.317089, with 3% account breaches. The output shows that a low hit rate can coexist with a fat right tail, but this is not a verified tradable expectancy. The diagnostic excludes profit-triggered SMA/ATR ratchet, monthly rank changes, 15-minute check delay, bids/asks, future dividends/cash, or novel tail shocks, and the five-year history omits 2020. The results are model-dependent first-passage diagnostics, not out-of-sample strategy performance, calibrated real win odds, Kelly inputs, or a guarantee. The mandate remains unchanged, and deployment should not be gated or targets changed based on this output. The sources are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=split, live signal 2903, and gap stress preceding statistical analysis.

The fund allocation has increased by 29.41% from 144.67 to 187.22, with equity remaining unchanged at 850.99. A proposed allocation of 62.40/62.41/62.41 is being considered. The crisis-inclusive period from 2016 to September 11, 2026, has been analyzed, with 2687 split-adjusted daily opens and prior closes. A loss scenario has been modeled, including a 2ATR stop, multiplicative adverse gap, and assumed 50bp friction. The formula used is L=N*[r+(1-r)*g+.005], where r is 2ATR/close. The worst gaps were found to be QQQ 9.457211%, IWM 9.301937%, and EFA 11.623512%, all on March 16, 2020. Gap correlations were QQQ/IWM 0.811520, QQQ/EFA 0.778810, and IWM/EFA 0.816941. Using DOUBLE, each worst gap gives 13.228414/13.002589/15.611427 dollars, which is 1.554474/1.527937/1.834502% equity each below the 25.5297 risk threshold. The joint total is 41.842430, which is 4.916912% equity. A common 20% extra beyond stop+50bp loses 41.364711, which is 4.860775% equity, and a common 10% loses 23.015787. Ordinary stops sum to 3.730764, and all initial targets yield 24.103442 gross, which is not the expected payoff. To reach a 3% per lot requires 39.071750/39.112107/39.405948% extra gap beyond stop under this equation. A total loss of 62.41 lot is 7.333811% equity, so there is no guaranteed 3% bound. An unconditional per-lot total-loss cap would need to be <=25.52 before fees. The conclusion is that the current equal lots pass explicit severe historical scenarios, but diversification does not imply a 3% bound. This is stress sizing, not a trade-edge backtest or probability forecast, and full-loss possibility and pre-2016/sample omissions remain. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=split&start=2016-01-01 and user venue/account context on September 12, 06:01 UTC.

A news scan found the following text: Premises include 1 completed September 11 daily bars fetched this run, 2 ETF total-return proxy uses SIP adjustment, 3 filter close above SMA200 then rank 252/365 interval returns, 4 context Core trend EEM1 at $67.84 queued, no other Core holdings or queues, own query trades empty, and 5 cap $187.22, account $850.99, position $212.75. Conclusion is initial complement QQQ, IWM, EFA. A table lists symbols, 12-month percentage, close, SMA200, ATR20, eligible rank, proposed dollars, reference stop, and reference target. The table includes: EEM with 34.20376% and $67.84 close, QQQ with 23.69878% and $714.88 close, IWM with 23.45726% and $288.89 close, EFA with 19.45813% and $106.7 close, SPY with 18.48906% and $764.29 close, BTC/USD with -33.16657% and $77212.8315 close, ETH/USD with -43.62279% and $2516.066 close, GLD with 18.94351% and $398.77 close, and TLT with -5.75690% and $80.87 close. ATR20 means average true range over 20 days, which is the mean of the maximum of high-low and absolute high-previous close and absolute low-previous close. Stop is actual entry minus 2 times ATR20 and target is actual entry times 1 plus 90th percentile rolling 60-day return. Selected 90th percentile returns are QQQ 16.169585%, IWM 12.823845%, and EFA 9.630283%. Reward to risk is 7.3454 to 6.0326 to 5.8326. Horizon is 1 to 3 months. Data sources are https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars, with adjustment definitions at https://docs.alpaca.markets/us/reference/stockbars.

A news scan found the headline about a diagnostic test called C91204 CLI. The test was run on a computer program using the command: node /app/agent/dist/backtest.js --symbols BTC/USD,ETH/USD,SOL/USD --strategy breakout --lookback20 --days980. This means the program looked at the prices of Bitcoin, Ethereum, and Solana over 980 days, using a strategy called breakout, which is a type of trading strategy that involves buying when the price breaks out of a certain range, and looked back 20 days to make decisions. The results were: for Bitcoin, 46.46% return, 14.69% maximum loss, and 34.33% of trades were winners; for Ethereum, 40.80% return, 24.94% maximum loss, and 30.91% of trades were winners; and for Solana, 72.15% return, 18.79% maximum loss, and 52.78% of trades were winners. For comparison, if you had just bought and held these currencies, the returns would have been: 75.66% for Bitcoin, 12.12% for Ethereum, and -35.31% for Solana. The program used a policy that looked at the closing prices of the last 20 days to make decisions, and did not include any fees or holdout periods. This diagnostic test is different from a validation test, and the results should not be used to choose which currency to trade. The data used for this test came from https://runway.adityaperswal.workers.dev/data/crypto-bars.

The account started with $850.99 and $153.18 in cryptocurrency, with no holdings or pending orders. The goal is to have a positive expectancy with a net payoff of 3R. The main constraint is the availability of entries, not a lack of capital. The first premise is based on an ETH hourly half ATR pullback managed with a 15-minute test train, which showed a 42.3123% return on 13 tests and a 19.2067% return on 3 holdout tests, with a stress test showing a 17.5499% return. However, the training mean confidence interval crosses 0, and the daily holdout shows a -0.2928% return. The second premise is that the literal complete 15-minute holdout does not destroy the result, with 18 missing points bound away from the stop and target by 2R. The third premise is that today's only candidate entry occurred on September 11 at 22 UTC before the study, and a late purchase would change the policy. The counterexample is that the price could still rise, but this does not validate a change in timing. The fourth premise is that the open interest is down 5.61% with a price down 4.29%, which is consistent with an unwind, but does not prove a rebound. The decision is to preserve the pullback candidate and reject an untested late entry. The validity of the decision supports the prospective candidate and does not prove that all cryptocurrency lacks an edge. The soundness is conditional on bar bounds, cost assumptions, and a limited sample. The Half Kelly from training is 1.299744, which clips the $153.18 fund. The operator needs $349.01 in outside risk. The decision is to adopt a research watch-only policy and not a new live entry policy. The first future event will be recorded before the outcome, and the original frozen breakouts will be assessed normally. Cash was chosen due to the absence of a fresh qualified entry, and not due to a blanket quarantine over missing bars. Local collectors passed, and 237, 238, and 239 were accepted through 06:30. The remote END is unverified. There is no proposal to change the fund. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars, https://www.okx.com/api/v5/rubik/stat/contracts/open-interest-history?instId=ETH-USDT-SWAP&period=1H&limit=100, and https://docs.alpaca.markets/us/docs/crypto-fees.

A review of the top 10 volatile coins from the latest 7-day volume census found that all of them were below their prior 480-hour high on September 12 at 03:00 UTC. The prices were: BTC $77,262.87, which is less than $82,233.50; ETH $2,513.18, which is less than $2,664.85; SOL $101.63, which is less than $110.54; LTC $54.01, which is less than $59.34; XRP $1.36, which is less than $1.55; HYPE $78.87, which is less than $89.99; LINK $11.49, which is less than $13.68; and UNI $6.15, which is less than $7.48. This review covered the full 480 hours and did not include any holdings or queued crypto orders. A frozen old ETH breakout from September 11 at 14:00 UTC was retained, with an entry price of $2,632.28, a stop price of $2,456.25, and a target price of $3,223.80, with a timeout on October 1 at 14:00 UTC. Between 03:00:00 and 03:45:56, 15-minute bars were completed, with a low of $2,506.24, and no stops or targets were touched, resulting in a loss of 4.76% and a gross loss of 0.68R. The position was closed at no profit. A new half ATR rule, which is a measure of price volatility, would have entered a position on September 11 at 22:00 UTC after the 21:00 UTC close at $2,515.68, with an entry price of $2,516.64, a stop price of $2,327.79, and a target price of $3,143.84, with a timeout on October 1 at 22:00 UTC. Over 24 bars, no stops or targets were touched, resulting in a loss of 0.39% and a gross loss of 0.74R. The position was closed at no profit. Both retrospective open paths were reviewed, but they were not actual orders and are not prospective out-of-sample orders. The premise of the review was that the current cheap price is not a fresh signal, and that a positive historical hourly payoff does not justify a 6-hour delay. The conclusion was that there are no exits to move and no fresh entries. A prospective event watch will review 239 records for actual availability at the next review. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the text describing the C91204 coherent daily reconstruction. This reconstruction uses eight predefined sensitivity cells to rebuild the daily high, low, open, and close prices of Ethereum, or ETH, from its 24 hourly prices. If a day is missing some hours, it is marked as incomplete and becomes unavailable, or NA, because the system requires a full 20-day history of the average true range, or ATR, and the highest price, or HIGH. The original files and 15-minute managed execution are kept. The first premise is that there are differences between the historical daily and hourly prices: on December 2, 2025, the ETH open price was 3020.1375, but the hourly aggregate was 2798.84, which is a difference of -7.3274 percent. In 2026, all 253 complete ETH days had matching daily high, low, open, and close prices. The second premise is that changes in the daily input data around a missing day in March affect the availability of the data, but this is a change in the data or control, not in the performance, or alpha. The hourly baseline remains unchanged at 42.312262 percent return, 37.521903 percent drawdown, 7 wins out of 13, and 1 open trade. The holdout set had a return of 19.206747 percent, a drawdown of 14.542745 percent, 3 wins out of 3, and 1 open trade. The stress holdout set had a return of 17.549943 percent. The daily holdout set changed from -0.292829 percent to -1.925730 percent, and the stress holdout set changed to -2.707198 percent. The daily training set remained unchanged at -27.126920 percent return. The conclusion is that issues with aggregating the data do not explain away the positive results of the hourly candidate. However, the daily negative results, cell selection, small sample size, and non-pristine holdout set still limit the deployment. All eight cells were recorded, bringing the total policy comparators to over 200, plus CLI3 diagnostics, but these are not 200 independent strategies or events. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars and the C91204aggregate_control.py file.

A 48-hour Ethereum block simulation was run with 20,000 paths, each with two resampled 24-hour blocks of 15-minute log returns, using 16,000 overlapping complete historical blocks from March 1 to September 12, 2004, UTC, resulting in 14,658 unique samples. This method preserves within-day dependence, breaks between days, omits missing blocks, and cannot invent unseen crashes. It is not conditioned on a pullback. The simulation started with a hypothetical completed spot price of 2513.175 at 03 UTC, with an entry price of 2515.688175 and a 10 basis point slip. The prior 20-day Average True Range (ATR), a measure of volatility, was 98.98817058. The stop loss was set at 2317.71183383 and the target at 3170.24364866, with a net gain of 3 times the risk after fees and slippage. The simulation checked every 15 minutes, with a quarter of the gross gain plus 2 times the risk if the stop loss was hit, and a full stop if the target was hit, with a 48-hour marked liquidation. The stop loss was hit first 3.9% of the time, the target was hit first 0.01% of the time, and a partial gain of 0.345% was achieved. The net 48-hour mean gain was -0.580697%, with a worst sample loss of -11.323239%. These 48-hour frequency figures cannot be used to price a 20-day thesis. However, using 965 overlapping daily windows from 2024 onward, the 20-day maximum high gain at the 90th percentile was 33.743953%, and the target gain of 26.0199% is within the measured range, although not guaranteed. The training data showed 7 wins and 6 losses out of 13 trades, with a win probability of 0.16101921 and a loss probability of 0.09851387, resulting in a half-Kelly bet of 1.299744. The capped risk notional was 153.18, with a quarter ceiling of 38.295 risks and a simulated worst-case loss of 3.224981 nominal USD, although the actual loss was 0 because the entry was fresh and the late policy was untested. The simulation used no leverage and no need-based sizing. The data sources were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline 'C91204 missing-checkpoint bounds preserve ETH modeled holdout; literal complete coverage remains zero'. This refers to an analysis of Ethereum trades from March 5-25, April 9-29, and July 7-27, which had 6, 3, and 9 missing 15-minute checkpoints, totaling 18. The term 'checkpoint' refers to a point in time when trade data is recorded. Despite these missing checkpoints, the analysis found that the primary high and low bounds excluded all initial stops, net 3R targets, and gross +2R touches. For example, on March 12 at 17 hours, four missing points used the completed day range of 2018.698-2094.2115, compared to the trade stop of 1791.83424 and +2R target of 2603.77278. The analysis also found that no imputed prices or zero returns were used. The condition that trade bar extrema covered missing intervals meant that all 18 missing checks could not change the three trade paths. The modeled holdout resulted in a 19.206747% return, with a 14.542745% drawdown and 3 out of 3 winning trades, and 1 open trade retained. However, a strong counterargument is that broader trade bars are not historical executable quotes and do not prove fills, check availability, or infinite liquidity. The training mean confidence interval still crosses 0. The validity of the analysis is that the bound proof supports no modeled threshold touch, not global data quality or trading edge. In conclusion, the analysis suggests that literal complete coverage alone should not be cited as a rejection, and the candidate should be kept prospective and separate from fresh entry gates. The full bounds recorded observations can be found in C91204gapbounds.py, sourced from https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the headline about C91204 uncertainty. The automated trading fund manager analyzed ETH hourly training with 13 closed trades, resulting in 7 wins, with a mean return of 4.123471%. The manager used a method called bootstrap resampling 10,000 times to estimate the range of possible mean returns, which was between -3.768357% and 12.803958%. Another method, called Wilson confidence interval, estimated the range of possible win rates to be between 29.1434% and 76.7942%. The manager also tested the system on a holdout set of 3 trades, which all won, with a mean return of 6.157475%, and a Wilson confidence interval of 43.8494% to 100%. However, the manager noted that even with these wins, it is not 100% certain that future trades will win. The manager also found that the average win was 16.101921% and the average loss was 9.851387%. The manager used a formula called the Kelly criterion to determine the optimal amount of capital to allocate, which was 1.299744 times the capital, but noted that this implies an impossible level of precision. The manager also analyzed the daily ETH training data, which had a mean return of -2.732972%, but the holdout set had a return of 0.024738% per trade, which compounded to a marked return of -0.292829%. The manager attributed this to something called volatility drag, which is not a contradiction. The manager also analyzed the correlation between BTC and SOL, and found that they have mixed and correlated market exposures, and are not independent samples. The manager used a statistical method called OLS to analyze the relationship between open interest and price, and found a correlation of 0.113948, and a beta of 0.069650, but did not claim to have found a causal relationship or significant prediction. The manager based the analysis on three premises: that the returned edge comes from a small few tails, that the same 2026 history is already known and many cells have been observed, and that frequencies are not replicates. The manager concluded that the system is a candidate for watching, but not for precise probability or blanket late-entry permission. The manager preserved all 200 cells, including coherence 8, and did not optimize on the holdout set. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://www.okx.com/api/v5/rubik/stat/contracts/open-interest-history?instId=ETH-USDT-SWAP&period=1H&limit=100.

A news scan found the note that Ethereum broke its prior 480-hour high of $2565.90, but later fell below its original level, and the fund has no position. The first consequence chain is that closing derivative positions can lower the open interest of the coin and may relieve leveraged selling, but it does not identify the side or liquidation. The second consequence chain is that lower funding or basis can also cause carry traders to close both short perpetuals and long spot, so a decline in open interest can create further spot pressure instead of a bottom. The third consequence chain is that a spot recovery requires renewed net demand relative to the available float, and stablecoin issuance, ETF shares, and protocol activity are not interchangeable with paid Ethereum buying. Market makers and patient buyers can benefit in certain scenarios. A counter chain is that strong independent spot demand can absorb forced sales, normalize the basis, and pay 3R if there is a renewed breakout pullback continuation. The hourly test is positive, but the daily test is negative and cross-asset results are mixed. These are conditional possible paths, not forecasts. The soundness of the analysis is based on observed open interest decline and cost tables, but trader identity, forced liquidations, and causal demand are unobserved. The conclusion is to freeze the prospective candidate and not make an ad-hoc late entry or open interest-based trade without an actual fresh signal. The source includes data from OKX, Circle, and Alpaca Markets.

A snapshot of Circle's issuer on September 10 showed that the company had 74.06 billion USDC, with 6.66 billion issued and 6.91 billion redeemed over the past 7 days, resulting in a net decrease of 250 million. Over the past 30 days, the company issued 38.06 billion and redeemed 36.04 billion, resulting in a net increase of 2.02 billion. These periods overlap, but neither is related to ETH purchase flow on September 11. On September 11, the open interest of ETH swaps on OKX fell from 655,754.752 to 618,960.851 between 14 UTC and 22 UTC, a decrease of 5.61%. The price of Alpaca also fell from 2,628.4075 to 2,515.682, a decrease of 4.29%. This decrease could be due to either long or short positions being closed, either voluntarily or by force. By September 12 at 04 UTC, the open interest had increased by 1.29% to 626,931.199, with a spot price of 2,513.175, suggesting that the decline may not be over. The latest 8-hour funding rate is 0.274278 basis points, which is at the 17th percentile of settlements between August 10 and September 12. A forecast of 0.8211 basis points is a separate field. In conclusion, while there is a plausible unwind mechanism, it is not a standalone cause for entry. A strong counterargument is that spot buyers can absorb the unwind and resume the uptrend, and hourly pullbacks have historically been positive. There is no evidence of new ETH buying from issuer receipts, ETF creation, or on-chain exchange flow. The primary sources of information are Circle and OKX, but they have commercial interests and may not reflect global leverage. A separate source, Llama, reported 74.46245 billion on September 12, but this is based on a different cutoff and classification, and does not resolve the contradiction.

The final decision for R91204 is to retain $170.20 in cash and to watch FTK for a potential cash conversion. The account started and finished with a supply of $170.20 in cash and $850.99 in total, with no open lots or pending supply orders. The goal is to achieve affordable bottleneck pricing power with a mandatory positive payoff and entry fail for FTK. Fundamental analysis of FTK shows international growth is credible, but exclusive pricing and cash capture are unproven. The company's H1 FCF is -7.819m, with a net settlement cash of 36.55% and an EV/guide EBITDA of 19.92x. Backtests were corrected and showed negative results for all 12 lookback/cost variants. Technical analysis showed a close of $27.33, which is less than the required $35.83 for entry. The monitor has been resolved and the collector has accepted the results. Research has reached three independent origin families and 12 question threads, but gaps remain in the data. No separate fund is justified and funding needs do not alter the risk or manufacture an order. The decision is based on data from https://www.sec.gov/Archives/edgar/data/928054/000092805426000080/ftk-20260630.htm and https://ir.flotekind.com/2026-09-09-Flotek-Raises-2026-Guidance-Again-on-Continued-Strength-of-International-Sales.

The automated trading fund manager has analyzed FTK and found that the current close of 27.33 is higher than the SMA50 of 26.8234, which meets the trend condition. However, the price of 27.33 is lower than the prior 20 high of 35.83, which fails the completed breakout condition. A frozen rule requires both conditions to be met before an entry can be made. Additionally, positive demand indicators such as International H2 forecast and Aramco's active Jafurah project do not provide information on FTK's actual price, volume, or cash receipts. The company's quarterly cash bridge shows a negative free cash flow of -0.981m in Q1 and -6.838m in Q2, which puts cash capture at odds with headline profits. The fund manager has also tested new RSI and volatility filters, but all 12 costed variants are negative, and technical history, current entry, and valuation all reject the entry. This evidence is against the tested entry, but it does not affect the entire business or all possible future strategies.

A diagnostic and sensitivity audit was performed on a trading strategy using the command line interface. The command used was to run a node with the following parameters: the file backtest.js, the symbol FTK, the breakout strategy, a lookback period of 20 days, a total of 2080 days, a stop loss percentage of 10, and a target percentage of 30. The results showed a return of 13.7774%, a drawdown of 49.9074%, and 66 trades with a win rate of 33.3333%. This test did not account for costs or adjustments and was not an independent holdout. The data used can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. A sensitivity audit was also performed with different parameters, including a 10, 20, and 40 period sensitivity, using a SIP adjustment, a warmup period in 2020, training from 2021 to 2024, and a holdout period from 2025 to September 11, 2026, with fees of 1 basis point and a slip of 5 or 15. All 12 tests had negative results. The correct open and close marks were used, superseding earlier drawdown only marks. The training drawdown for the 10 period sensitivity was 86.7031% and 87.1476%, while the holdout drawdown was 46.9088% and 47.8559%. The training drawdown for the 40 period sensitivity was 59.3906% and 60.6103%, while the holdout drawdown was 31.7072% and 32.8585%. The sample size for the 10 period sensitivity was 24 trades for training and 15 trades for holdout, each with 1 unfinished trade. The sample size for the 40 period sensitivity was 16 trades with no change in returns from 2847 to 2850 and 2855 to 2858. There was no favorable lookback selection or strategy adoption. The command line interface sample of 66 unadjusted trade histories and SIP costed samples answer different questions.

This update corrects the marks for FTK as of 2025-01-01 with a 15bp change. It only changes the drawdown in the main analyses, which are sections 2851-2854, and keeps the rule from 2531 that freezes certain values. The original marks used the closing prices for both parts, which incorrectly marked the exit day close before the modeled intraday exit. The corrected marks use the actual open and close prices, alternating between them, with entry happening in the first slot and exit in the second slot. The drawdown marked by open and close is not the full worst-case scenario within a day. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all. The strategy uses a close breakout with a 20-period limit and a 50-period simple moving average, with the next open, a 2 average daily range stop, and a 3 times return target, over 40 sessions, with 1 basis point fees and 15 basis points slip per side, from 2020 for warm-up, and from 2025-01-01 to 2026-09-11. There were 14 completed trades and 0 open trades; the strategy stops at the first ambiguous bar, allows gap open exits, and retains entry day exits. The compounded cash return reconciliation passed. The strategy still does not allow trading due to the absence of 15-minute management and a plus 2 times return management. All 12 sensitivity revised drawdown tables have been updated in the latest observation without retuning any parameters.

This correction replaces the previous drawdown analysis in sections 2851-2854, but keeps the rule that was in place since 2531, including samples, fills, and returns. The original analysis used closing prices for both entry and exit points, which incorrectly marked the exit day close before the modeled intraday exit. The corrected analysis uses actual open and close prices, alternating between the two, with entry occurring at the start of the period and exit at the end. This means that the drawdown, which is the decrease in value, is not the full worst-case scenario for the entire day. The data comes from a stock market data source. The trading strategy used is based on a 20-day closing price breakout and a 50-day moving average, with a stop loss and target return. The strategy was tested from January 1, 2025, to September 11, 2026, with 14 completed trades and no open trades. The analysis still does not recommend trading due to missing risk management and other factors. All sensitivity analyses have been revised in the latest observation, with no changes to the parameters.

This update replaces the previous drawdown information in analyses 2851-2854, but keeps the rule that was in place since 2531, along with samples, fills, and returns. The original marks used closing prices for both entry and exit, which incorrectly marked the exit day close before the modeled intraday exit. The corrected marks now alternate between actual open and close prices, with entry happening in slot 0 and exit in slot 1. This means the drawdown marked by open and close prices does not show the full worst-case scenario that could happen within a day. A news scan found the data source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all. The trading strategy used is L20 close breakout with a 50-day Simple Moving Average, next open, 2 Average Daily Range stop, and a 3 Reward target, over 40 sessions, with 1 basis point fees and 15 basis points slip per side, from 2020 warmup to 2021-01-01 to 2024-12-31. There were 26 completed trades and 0 open trades. The strategy stops at the first ambiguous bar, retains gap open exits and entry day exits. The compounded cash return reconciliation passed. However, the strategy still rejects trading due to the absence of 15-minute management and a +2 Reward management. All 12 sensitivity revised drawdown tables have been updated in the latest observation without any parameter retuning.

A news scan found the text: Supersedes drawdown only in primary analyses 2851-2854; retains frozen 2531 rule, samples, fills and returns. Original two-slot marks used closing prices in both slots, incorrectly marking exit-day close before modeled intraday exit. Corrected vectorbt, a backtesting library, price marks alternate actual open/close, while entry occurs slot 0 and exit slot 1; open/close marked drawdown is not full intraday worst case. Source is a data line from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=FTK&timeframe=1Day&feed=sip&adjustment=all. The strategy uses L20 close breakout and SMA50, next open, 2 ADR stop, and 3R target, with 40 sessions, 1bp fees, and 5bps slip per side, from 2020 warmup, 2021-01-01 to 2024-12-31. There were 26 completed trades and 0 open trades; stop first ambiguous bar, gap open exits, and entry day exits were retained; compounded cash return reconciliation passed. The system still rejects trading; 15min and +2R management are absent. All 12 sensitivity revised DD table in latest observation; no parameter retuning was done.

The notes are about a trading strategy for SOL. It uses a 15-minute bar-open point-check proxy. The strategy takes 25% of the profit at gross+2R and sets the remaining stop to the entry point, with a net 3R target. It checks for a half-ATR pullback within 3 days of the first prior 20-day high breakout. The stop is set to 2 prior-completed-day ATR20 with a 20-day timeout. The buy fee is 25bp credited in coin and the sell fee is 25bp in USD. There is a 10bps lip baseline and 30bps stress on each side. The training period from 2024-25 had a return of 10.5767%, a drawdown of 35.0750%, and a win rate of 57.14285714285714%. The holdout period from Jan 1 to Sep 11 2026 had a return of -4.6600%, a drawdown of 21.7915%, and a win rate of 50%. The stress was -5.4197%. The immediate breakout control had the same cost and exit as the holdout, with a return of 4.0652% and 6 trades. The strategy has three premises: different timeframes and policies, small samples do not establish a prospective edge, and missing observations are skipped. The vectorbt.from_orders cash is reconciled with explicit credited fee conversion. The SOL history starts from Aug 2024 and has a shortened training period. The source of the 15-minute open is a trade bar proxy, not historical executable quotes or actual venue fills. The conclusion is that this is a research comparator and not an automatic entry strategy. All 192 cells are retained in the observations. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

The notes are about a trading strategy for the SOL cryptocurrency. It uses a 15-minute bar-open point-check proxy, which is a way to estimate the opening price of a trade. The strategy takes 25% of the profit at a certain point, called gross+2R, and sets a stop loss at the entry point for the remaining amount, with a target of net 3R. This means it aims to make three times the risk amount. The strategy checks for a half Average True Range (ATR) pullback, which is a measure of price volatility, within three days of a breakout from a 20-day high. ATR is a way to measure how much prices move. The stop loss is set at two times the prior completed day's ATR, with a 20-day timeout. There are fees for buying and selling, 25 basis points in coin and 25 basis points in USD, with a 10 basis point slip baseline and 30 basis point stress on each side. Basis points are a way to measure small percentages. The strategy was trained on data from 2024-25 and had a return of 27.9720%, a drawdown of 19.8678%, and a win rate of 75%. Drawdown is the maximum loss from a peak to a trough. The holdout period from January 1 to September 11, 2026, had a return of -12.1890%, a drawdown of 18.3921%, and a win rate of 33.3333%. The stress test result was -13.0386%. The immediate breakout control had the same cost as the exit holdout, with a result of -23.7294% and seven trades. The strategy is based on two premises: that different timeframes and policies cannot be substituted for older models, and that small samples and known hypothesis discovery do not establish a prospective edge. Edge refers to the ability to make profitable trades. The third premise is that missing observations are skipped or entries are cancelled, not treated as zero returns. The vectorbt library is used to reconcile cash from orders, and the end open positions are marked without invented close prices. The SOL history starts in August 2024, with shortened training. The source of the 15-minute open data is a trade bar proxy, not historical executable quotes or actual venue fills, which means it's an estimate of the opening price. Gaps in the data can delay exits. The conclusion is that this is a research comparator, not an automatic entry strategy, and all 192 cells are retained in the observations. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

The notes from the automated trading fund manager are as follows. The manager uses a 15-minute bar-open point-check proxy, which is a way to estimate the opening price of a trade. The manager takes 25% of the profit at a gross return of 2R, which is a measure of the profit relative to the risk, and sets the remaining stop to the entry point, with a net target of 3R. The manager checks for certain conditions before making a new entry. The strategy involves a half-ATR pullback, which is a price movement that is half the average true range, a measure of price volatility, within 3 days of a prior 20-day high breakout. The stop loss is set at 2 times the prior completed day's average true range, with a 20-day timeout. The manager also considers buy and sell fees, with a 25 basis point fee credited in coin and a 25 basis point fee in USD, and a 10 basis point slip baseline and 30 basis point stress on each side. The training period from 2024 to 2025 showed a return of 42.3123%, a drawdown of 37.5219%, and a win rate of 53.84615384615385%, with 13 closed trades and 1 open trade. The holdout period from January 1 to September 11, 2026, showed a return of 19.2067%, a drawdown of 14.5427%, and a win rate of 100%, with 3 closed trades and 1 open trade, and a stress test result of 17.5499%. The manager notes that the premise of the strategy is that different timeframes and policies differ, and that there is no substitution for the older frozen ETH model. The manager also notes that small samples and known 2026 hypothesis discovery do not establish a prospective edge, and that missing observations are skipped and entries are cancelled, not zero returns. The manager uses a vectorbt.from_orders cash reconciled with explicit credited fee conversion, and marks end open positions without invented close. The source of the 15-minute open is a trade bar proxy, not historical executable quotes or actual venue fills, and gaps can delay exits. The conclusion is that this is a research comparator, and not an automatic entry, and all 192 cells are retained in observations. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the following notes from an automated trading fund manager. The manager uses a 15-minute bar-open point-check proxy, which is a way to estimate the opening price of a trade. The manager takes 25% of the profit at a gross return of 2R, which means twice the risk, and sets the remaining stop to the entry point, with a target of 3R. The manager checks for certain conditions before making a new entry. One condition is a half-ATR pullback, which means a price drop of half the average true range, within 3 days of a prior 20-day high breakout. The stop is set at 2 times the prior completed day's average true range, with a 20-day timeout. The manager also considers fees, with a buy fee of 25 basis points credited in coin and a sell fee of 25 basis points in USD. The manager uses a 10 basis point slip baseline and 30 basis point stress on each side. The training period from 2024 to 2025 had a return of -27.1269%, a drawdown of 37.0512%, and a win rate of 37.5%, with 8 closed trades and 1 open trade. The holdout period from January 1 to September 11, 2026 had a return of -0.2928%, a drawdown of 21.3949%, and a win rate of 66.66666666666666%, with 3 closed trades and 0 open trades, and a stress of -1.4822%. The manager notes that the immediate breakout control had the same cost as the exit holdout, with a return of -12.7035% and 5 trades. The manager makes three premises: that different timeframes and policies differ, that small samples and known 2026 hypothesis discovery do not establish a prospective edge, and that missing observations are skipped and entries are cancelled, not zero returns. The manager uses vectorbt to reconcile cash from orders and credits fees, and marks end open positions without invented close. The source of the 15-minute open is a trade bar proxy, not historical executable quotes or actual venue fills, and gaps can delay exits. The manager concludes that this is a research comparator and not an automatic entry, and all 192 cells are retained in observations. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.