A simulation called C91206 uses a set of historical data, specifically 15-minute log returns from March 1 to September 12, to test trading strategies for Bitcoin, Ethereum, and Solana. This simulation uses 18,744 calendar rows of data, but is missing some data for Bitcoin, Ethereum, and Solana. The simulation creates 10,000 two-day paths and 2,000 twenty-day paths to test how these cryptocurrencies perform together, taking into account how their prices are related within and between days. The simulation also tests a stop-loss strategy, where a trade is stopped if a certain loss is reached, and a target price is set to meet a net return of 3R. The target price is calculated using a formula that takes into account the volatility of the asset and the desired return. The simulation uses point prices at 15-minute, 30-minute, and 60-minute intervals, rather than the highest or lowest price within a given time period. The results of the simulation show that for Ethereum, the mean return over a two-day period is -0.67%, and the target price is 0%. Over a twenty-day period, the mean return is -0.39%, and the target price is 3.35%. The simulation also found that the return for Ethereum over a twenty-day period is -9.92% when using a 1-minute interval, compared to -10.47% when using a 60-minute interval. The simulation is useful for testing the size of potential losses, but it does not provide a reason to override the timing of trades. The data used for the simulation is available at https://runway.adityaperswal.workers.dev/data/crypto-bars.
A trading fund manager started with $153.18 in cash, aiming to find an independent price edge. The manager considered three premises. First, as of September 10, Circle, the issuer of USDC, reported $74.06 billion in USDC, with $6.66 billion issued and $6.91 billion redeemed in the last 7 days, and $38.06 billion issued and $36.04 billion redeemed in the last 30 days. This resulted in a net loss of $0.25 billion in 7 days, or $35.714 million per day, but a net gain of $2.02 billion in 30 days, or $67.333 million per day. This shows that even with a short-term loss, the longer period still saw an increase. Second, an aggregator reported $74.46245 billion in USDC on September 12, but this number is based on a different cutoff and construction, and cannot resolve issuer definitions. Third, stablecoin holders may use their coins to settle payments or move to different venues without buying other, more volatile tokens. The manager concluded that while this information is useful for monitoring liquidity, it is not enough on its own to decide to buy ETH or SOL. The primary issuer benefits from larger balances and reserve income, while holders benefit from being able to use the coins for settlement. For the tokens to appreciate in value, there needs to be additional demand. The manager used data from https://www.circle.com/transparency and https://stablecoins.llama.fi/stablecoin/2. No new information was found that would change the current situation, and the manager's existing monitors were preserved. The manager would only change their neutral price implication if they saw synchronized native supply, paid fee capture, and independently costed forward returns.
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 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.
The notes are about a trading strategy for Bitcoin, referred to as C91204. It uses a 15-minute bar-open point-check proxy, which is a way to track 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, with a target of net 3R. It checks for certain conditions before entering a new trade. One condition is a half-ATR pullback, which is a price movement of half the average true range, within 3 days of a prior high point. The stop loss is set at 2 times the average true range of the prior 20 days, with a 20-day time limit. The strategy also takes into account fees, such as a 25 basis point buy fee credited in coin and a 25 basis point sell fee in USD. The training period from 2024 to 2025 had a return of -9.96%, a drawdown of 33.29%, and a win rate of 43.75%. The holdout period from January 1 to September 11, 2026 had a return of 1.19%, a drawdown of 10.12%, and a win rate of 66.67%. The strategy is based on certain premises, including that different time frames and policies should not be substituted for older models. The data source is a 15-minute open trade bar proxy, which may not reflect actual executable quotes or venue fills. The conclusion is that this strategy is for research comparison only and does not automatically enter trades.
The notes are from a frozen system, numbered 2535 and 2536. This system uses a 15-minute bar-open point check as a proxy. It takes 25% of the profit at a gross return of 2 times the risk, and sets the remaining stop to the entry point, with a target of 3 times the risk. The system checks for these conditions before making a new entry. It looks for a half Average True Range pullback within 3 days of the first breakout above the 20-day high. The stop is set at 2 times the prior completed day's Average True Range, with a 20-day timeout. The buy fee is 25 basis points, credited in coin, and the sell fee is 25 basis points, paid in USD. There is 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 -14.7836%, a drawdown of 26.5712%, and a win rate of 20%, with 5 closed trades and 0 open trades. The holdout period from January 1 to September 11, 2026, had a return of 6.8464%, a drawdown of 8.7633%, and a win rate of 66.6667%, with 3 closed trades and 0 open trades, and a stress of 5.5719%. The immediate breakout control had the same cost as the exit holdout, with a return of -8.3834% and 6 trades. There are three premises: different timeframes and policies do not substitute for older models, small samples and known hypothesis discovery do not establish a prospective edge, and missing observations are skipped and entries are cancelled, not treated as zero returns. The vectorbt system reconciles cash from orders with explicit credited fee conversion, and marks end open positions without invented close prices. The source of the 15-minute open data is a trade bar proxy, not historical executable quotes or actual venue fills, and gaps can delay exits. This is a research comparator and not an automatic entry system, with all 192 cells retained in observations. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the notes of a trading fund manager. The manager used a system with daily and hourly data to decide when to enter and exit trades. The system was designed to buy after a pullback, which is a drop in price, within three days of a new 20-day high. The pullback had to be half of the average true range, a measure of price volatility, over the past 20 days. The manager also set a stop-loss, which is a price level at which to automatically sell, at two times the average true range of the previous 20 days. If the trade was not successful within 20 days, it was closed. The manager paid a 0.25% fee when buying and selling, and assumed an additional 0.1% to 0.3% slippage, which is the difference between the expected and actual price of a trade. From 2024 to 2025, the system had a return of -6.34%, a maximum drawdown of 37.68%, and a win rate of 42.86%. It closed seven trades and had no open trades. From January 1 to September 11, 2026, the system had a return of -3.78%, a maximum drawdown of 20.46%, and a win rate of 50%. It closed two trades and had no open trades. The manager noted that the system's performance may not be representative of future results, and that the sample size was small. The manager also noted that missing observations were skipped, and that the system did not substitute for an older model. The data used was from August 2024, and the manager reconciled the cash balance with the credited fees. The manager concluded that the system was for research purposes only, and not for automatic trading. 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 notes of a trading fund manager. The manager used a strategy called OHLC, which stands for Open, High, Low, Close, a way to show daily and hourly stock prices. The manager also used something called ATR, which means Average True Range, a measure of how much a stock price moves. The strategy was to buy when the stock price pulled back by half of its average movement within three days of reaching a new high. The manager would sell when the stock price reached a certain level or after 20 days. The manager paid a fee of 0.25% to buy and sell. From 2024 to 2025, this strategy made a return of 13.23%, but lost 19.79% at its worst. The strategy won 50% of the time and closed four trades. From January 1 to September 11, 2026, the strategy made a return of -11.01%, lost 15.71% at its worst, and won 33.33% of the time. The manager notes that this strategy is not a substitute for an older strategy used for Ethereum. The manager also notes that the results are based on a small sample and do not guarantee future success. The manager used data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the notes of an automated trading fund manager. The manager used a strategy called C91204 ETH 1Hour half-ATR pullback OHLC exits. This strategy is based on a daily and hourly proxy with specific entry and exit rules. The rules include a half Average True Range, or ATR, pullback within 3 days of a breakout from a 20-day high. The ATR is a measure of price volatility. The strategy also includes a stop-loss order set at 2 times the prior completed day's ATR, with a 20-day timeout. The manager accounted for a 25 basis point buy fee credited in coin and a 25 basis point sell fee in USD, with a 10 basis point slippage baseline and 30 basis point stress on each side. The results of the strategy from 2024 to 2025 showed a return of 59.1821%, a drawdown of 34.8121%, and a win rate of 53.84615384615385%, with 13 closed trades and 1 open trade. From January 1 to September 11, 2026, the return was 19.2067%, the drawdown was 13.7909%, and the win rate was 100%, with 3 closed trades and 1 open trade, and a stress test result of 17.5499%. The manager noted that the strategy's performance may not be representative of future results and that the sample size is small. The manager also noted that missing observations were skipped and not treated as zero returns. The data used for the strategy was sourced from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
These are notes about an automated trading system for Ethereum, also known as ETH. The system uses a daily and hourly proxy with a specific entry and exit strategy. It looks for a half Average True Range, or ATR, pullback within 3 days of a breakout from a 20-day high. The ATR is a measure of how much a price is moving. The system stops trading if it loses a certain amount, specifically 2 times the ATR of the last 20 days, and it has a 20-day time limit. The system also accounts for fees, including a 25 basis point buy fee credited in ETH and a 25 basis point sell fee in USD. A basis point is a unit of measurement for interest rates and fees. The system was trained on data from 2024 to 2025 and had a return of -23.6886%, a drawdown of 34.2138%, and a win rate of 37.5%. It closed 8 trades and had 1 open trade. From January 1 to September 11, 2026, the system had a return of -0.2561%, a drawdown of 19.8702%, and a win rate of 66.6667%. The system is not meant to be used for automatic trading and is for research purposes only. The data used to train the system came from 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 Bitcoin, called C91204. It uses a daily and hourly proxy with a specific entry and exit method. The strategy buys when the price pulls back by half of the average true range within three days of a new 20-day high. It stops the trade at two times the average true range of the previous 20 days, with a 20-day time limit. The strategy also considers fees, with a 0.25% buy fee credited in Bitcoin and a 0.25% sell fee in USD. There is a 0.10% baseline slip and a 0.30% stress slip on each side. The strategy was trained from 2024 to 2025 and had a return of -17.09%, a drawdown of 31.95%, and a win rate of 41.18%. It closed 17 trades and had no open trades. From January 1 to September 11, 2026, the strategy had a return of 1.30%, a drawdown of 9.54%, and a win rate of 66.67%. It closed 3 trades and had 1 open trade, with a stress percentage of 0.11%. The strategy is compared to a control group with the same costs and exits, which had a return of 5.77% in the holdout period. The strategy is based on two premises: that different time frames and policies cannot be substituted, and that small samples and known hypothesis discovery do not establish a prospective edge. A third premise states that missing observations are skipped and entries are cancelled, not treated as zero returns. The strategy uses a vectorbt library to reconcile cash and credited fees, and marks open positions without invented closes. The intrabar target and stop fills are optimistic about continuous availability, despite conservative ambiguous bar ordering. The conclusion is that this is a research comparator, not an automatic entry strategy, and all 192 cells are retained in observations. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the notes from a trading fund manager. The manager used a system with daily and hourly data to decide when to buy and sell. The system used something called OHLC, which stands for Open, High, Low, Close, to determine when to enter and exit trades. It also used a half-ATR pullback, where ATR means Average True Range, a measure of how much prices move. The system would buy when the price pulled back by half of the average true range within three days of a breakout, which is when the price moves above a previous high. The system would stop buying if the price moved below the average true range of the previous 20 days, and it would stop trading after 20 days. The manager paid a fee of 25 basis points, or 0.25%, when buying and selling, and allowed for a 10 basis point slip, or 0.10%, on each side. The system was trained on data from 2024 to 2025 and had a return of -17.0123%, a drawdown of 26.2433%, and a win rate of 20%. It closed five trades and had no open trades. From January 1 to September 11, 2026, the system had a return of 6.8464%, a drawdown of 7.7723%, and a win rate of 66.66666666666666%. It closed three trades and had no open trades, with a stress test result of 5.5719%. The manager noted that the system should not be used as a substitute for an older system used for Ethereum, and that the results may not be reliable due to the small sample size. The manager also noted that missing observations were skipped and not treated as zero returns. The system used data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
The account starts with $850.99 and 153.18 in crypto cash, with no lots. The goal is to determine if a failed-breakout pullback has a positive executable expectancy. A failed-breakout is when a stock or crypto fails to break through a resistance level, and a pullback is when it then falls back. The test has a binding constraint, meaning it must follow a specific entry-policy validation, not the operator shortfall, which refers to the difference between the expected and actual performance of a trading strategy. The test notes that the full three-level question tree and 0.5 ATR dip rule, where ATR stands for Average True Range, a measure of volatility, will be used, along with 0.25/1 ATR neighbors, which refers to the distance between price levels. The test will use BTC/SOL, or Bitcoin and Solana, as controls, and will train on data from 2024-25 and hold out data from 2026. A 25 basis point credited fee, or 0.25%, and a 10/30 basis point slip, or 0.1/0.3%, will be used. The premises of the test are: 1) a cheaper entry can reduce the price paid, but may condition on weaker future demand, 2) the latest ETH path, or Ethereum price movement, motivated the question, and 3) daily/hour intrabar stops, or price levels at which to stop trading, differ from actual 15-minute checks. The validity of the test depends on testing independent repeatable events, and soundness depends on completed timestamps, missing-bar controls, and costed positive payoff across regimes. The prior improvement probability is unspecified and roughly even, and is not used for money. New evidence that would matter includes coherent net improvement across both frequencies and BTC/ETH controls with confidence intervals excluding 0, then prospective events. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. No order is authorized by this research design alone.
The automated trading fund manager made a final decision on C91202. The account started with $850.99 in equity and $153.18 in deployable cash, with no holdings or queued orders. The goal is to have a positive executable expectancy that can absorb weekend volatility. A key constraint is not to make a new entry under the current frozen policy, as a cheaper late entry has not been validated. The manager considered several factors, including the performance of volatile coins, frozen ETH hourly evidence, and primary liquidity evidence. Based on these factors, the decision was made not to place new orders and to retain the current $153.18 in cash. The manager also widened the monitored universe to keep track of potential opportunities. The decision is based on logical inference, but its empirical soundness is limited by costs, history, and sample size. The strongest counterargument is the potential missed upside from waiting, which will be recorded as an opportunity cost and used to improve monitoring.