The automated trading fund starts with $235.86 in crypto and $850.99 in equity, aiming for a repeatable net profit. There are three premises: 1) Two explorer tips, unchanged at 4051232/4050335, aged 184353/238173 seconds as of 08:01, with the operator still preparing to restart. The reserve has 4.000004 BTC in the mempool, but payout proof is not authorized, and confirmations and controller evidence are needed. 2) Issuer stock, ETF shares, and GME accounting do not time spot orders, based on fundamental analysis from 1628. 3) Published on September 9, compensation and services at 14:00 UTC, and on September 10, PPI at 12:30 UTC, wholesale at 14:00 UTC, and EIA at 16:00 UTC are scheduled risk contexts, not directional entry edges. The first order considers that reliable recovery would benefit trapped claimants, and stablecoin payments would benefit issuers and market makers, but macro surprises can change observed costs and activity. The second order notes that optional BTC, ETH, and SOL flows depend on fund destinations and allocation, with substitution, payments, and hedging counterexamples. The third order warns that price impact may precede publication, and paying approximately 0.5% fees plus spread to chase context can erode cash. A strong counterargument is that a rebound underway could be missed, acknowledging opportunity cost without inventing an edge. Conclusions are valid given the mandate gate, but soundness is limited by explorer lag, issuer bias, unknown custody authorization, and no matched flow price history. New verified payouts or dated new spot flow updates can change premises, but only independent daily and hourly net edge and 15-minute execution can change size. Sources include https://blockstream.info/liquid/api/blocks, https://liquid.network/api/blocks, https://x.com/Blockstream, https://www.bls.gov/schedule/2026/09_sched.htm, https://www.census.gov/economic-indicators/calendar-listview.html, https://www.eia.gov/petroleum/supply/weekly/schedule.php, and https://docs.alpaca.markets/us/docs/crypto-fees.
An automated trading fund manager performed a liquidity audit at 08UTC. The audit found that changes in accounting do not establish new spot demand. The manager looked at several pieces of data, including the circulation of Circle on September 7, which was 74.28 billion, and the issuance and redemption of 8.8 billion and 7.86 billion, respectively, over seven days. This implies an average daily increase of 134.286 million. However, the manager does not know who is buying or where the funds are going. On September 9, the manager checked the USDC stock at 08:01 and found it was 74.381077962 billion, which is 55.678 million lower than the stock at 06:16. The manager also looked at the stock of Llama on September 9 and found that it was 74.502816709 billion, which is different from the chain population. The manager notes that there is no contradiction once the dates and definitions are separated. The manager also looked at the shares of IBIT and ETHA on September 8 and found that they were 1.38756 billion and 467.68 million, respectively, which is similar to the quantities on September 4. This means that there is no new dated spot flow. The manager also reviewed the release of GME on September 8, which showed a loss of 75 million in the quarter ended August 1, and cash and marketable securities of 5060.3 million, and debt of approximately 2800 million after the exchange on September 3. However, this does not establish a forced sale. The manager concludes that they will retain a liquidity watch but with an allocation of 0. The manager notes that without knowing the destination or timing of the funds, they cannot estimate the impact of entry. The manager also notes that actual purchases may precede disclosure, and the absence of proof is not proof of absence. New disclosure with BTC quantities, counterparty, and timing would update the context, but an independent price edge is still required. The manager used several sources, including the websites of Circle, USDC, and Llama, as well as the SEC website.
At 06UTC, the automated trading fund manager made a decision based on the current state of the market. The fund had $850.99 in equity and $235.86 in cash, with no open or queued lots. The goal is to achieve a repeatable net profit, with a binding constraint of validated entry payoff. The premises for this decision were: 1) 144 costed vector backtests were run with 2025 training and 2026 holdout data, and the next open, fees of 25 basis points per leg, and 5/15 basis points slip did not qualify for a frozen RSI trend pullback. A basis point is a unit of measurement for interest rates and fees, equal to 0.01%. 2) The daily baseline returns for BTC, ETH, and SOL were +0.18177%, -3.75826%, and -3.44512% respectively, with hourly returns of +0.62703%, +4.48647%, and +2.05713%. The positive baseline confidence intervals included negative payoffs, with less than 30 samples. BTC stress was -0.41752% daily and -0.17478% hourly. ETH and SOL hourly training data showed -17.0122% and -23.4945% returns, with 6 missing held hours for SOL. 3) An ordinary least squares joint test and simulation could not rescue the sample, and there was no current completed September 8 entry. 4) A review of due flow and recovery found no verifiable new spot buying or authorized recovery, with quotes showing recovery hurdles of 0.5385%, 0.5309%, and 0.6303%. The conclusion was to not propose, exit, or adjust, with an approved Kelly criterion of 0 and $235.86 in cash. The validity of this decision is based on the mandate requiring a validated edge, which is absent here. The soundness limits of this decision include reconstructed historical bars, reused calendar across research, and unmeasured microstructure, with bootstrap serial dependence. A strong counterargument was found in SOL's daily unfiltered return of +14.1017%, but it failed the confidence interval and hourly return tests. The answer will only be changed with prospective independent 30+ samples, costed positive uncertainty bound, and daily/hourly and 15-minute execution validation. There was no fresh dated catalyst trade. The monitor showed 116 resolved and 120 at 08UTC, with jobs 123-125 requested and the first saved output pending at 06:08. Manual scripts were verified, and 14 out of 25 were retained. Lessons were recorded for technical, execution, and psyche aspects. The sources used were https://runway.adityaperswal.workers.dev/data/crypto-bars, https://docs.alpaca.markets/us/docs/crypto-fees, https://www.circle.com/transparency, https://www.usdc.com/, and https://blockstream.info/liquid/api/blocks. The process finished in approximately 12 minutes under the 43-minute allowance.
A news scan found the following data: as of 06:09, the bid and ask prices for Bitcoin, Ethereum, and Solana were 78903.9 and 78932.666, 2495.48 and 2496.2, and 104.055 and 104.188, respectively. The spreads, which refer to the difference between the bid and ask prices, were 3.645, 2.885, and 12.774 basis points, or 0.03645%, 0.02885%, and 0.12774%. The fees for these assets were 0.5385%, 0.5309%, and 0.6303%. The ask sizes, which are the amounts of the assets available for purchase, were 78.65, 82.99, and 98.98 USD, below the position cap of 212.7475 USD. The top quote, or the best available price, was not enough to fill the full position cap. The HYPE asset had a displayed ask price of 1.54 USD, despite having a large amount of earlier depth, which refers to the amount of buying and selling interest at different prices. Time variation, or changes over time, mattered in this case. The quotes for the stablecoins USDC and USDG were stale, or outdated, with 8 out of 10 being fresh, or up-to-date. The latest completed daily Average True Range, which is a measure of volatility, was 2271.071824 for Bitcoin, 88.760494 for Ethereum, and 5.6218 for Solana, which corresponds to approximately 2.8950%, 3.5725%, and 5.4390% of the closing price. A stop, or a price level at which to sell, and a target, or a price level at which to take profit, were set at 1 and 1.5 times the Average True Range, but this was only for diagnostic purposes. The planned notional risk, or the amount of money at risk, was limited to 8.5099 USD and 235.86 USD in cash. Stop checks, which are checks on the price every 15 minutes, are not continuous guarantees. There was no current frozen Relative Strength Index entry, which is a measure of the magnitude of recent price changes. The Kelly estimate, which is a measure of the optimal amount to invest, was not supported by the data. The approved size was 0. The top 10 volume inventory, which is a list of the most traded assets, was unchanged, and included USDC/USDG and HYPE. The USDG asset only had 4 out of 7 days of history, so its rank did not imply eligibility. The only asset owned was a BTC trade operator that was liquidated, resulting in a loss of 0.081%. There were no holdings, so a review of the target progress and horizon was not applicable. The source of this data is https://runway.adityaperswal.workers.dev/data/crypto-snapshots, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://docs.alpaca.markets/us/docs/crypto-fees.
The CLI RSI smoke test is a diagnostic tool, not a validation that includes costs. A test was run using the node /app/agent/dist/backtest.js with the following parameters: symbols BTC/USD, ETH/USD, and SOL/USD, strategy rsi, period 2, low 10, high 60, and days 616. The test retrieved 617 bars, including the current partial bar as of September 9, from the source https://runway.adityaperswal.workers.dev/data/crypto-bars. The results showed an uncosted return of -14.5454% for BTC, +53.1478% for ETH, and -28.7169% for SOL, with a maximum drawdown of 33.2792% for BTC, 40.4938% for ETH, and 43.7119% for SOL. The number of trades was 87 for BTC, 93 for ETH, and 87 for SOL, with an average trade percentage of -0.14368 for BTC, 0.57454 for ETH, and -0.29181 for SOL. A buy and hold strategy would have resulted in a return of -16.4412% for BTC, -25.5942% for ETH, and -46.3200% for SOL. An inspection of the engine revealed that the close signal execution was the same, with no fees and no split, but the CLI RSI logic differs from the frozen persistent position rule. Therefore, the 53% return for ETH cannot be used to justify trading. A future experiment, real costed next open vector backtest 1536-1541, will use 2025 as the training period and 2026 as the holdout period, and will include completed days, fees, slip, and trend/time exit. The rejection of the CLI as a validation tool does not claim that future returns for ETH will be negative. The statistics provided are for an illustrative uncosted diagnostic test, not for actual fund performance, and come from the local CLI engine and https://docs.alpaca.markets/us/docs/crypto-fees.
The fund started with $850.99 in equity and $235.86 in cryptocurrency, with no current holdings or orders. The goal is to make a repeatable profit in cash. However, there are limitations due to the precision of evidence and execution, rather than the amount of available capital. There are three premises: 1) raw liquidity snapshots do not identify new buyers, 2) a pullback of 1-5, not 30, is the minimum, and all positive baselines include uncertainty, with a negative cost stress for Bitcoin, and 3) sampling 10,000 paths does not reduce uncertainty. The first order of analysis shows that stablecoin issuers, payment users, and market makers benefit from circulation and turnover, with takers paying two fees, around 0.5% before spread. The second order of analysis indicates that only new spot purchases or protocol fees may benefit major coins, while payments or rotation can offset. The third order of analysis reveals that chasing small positive backtest estimates increases costs and reduces the runway, while preserving cash allows for new flow and price edges. Venue and liquidity providers benefit from increased turnover, not necessarily the fund. A counterargument is that holding cash may mean missing a continuing rally, but the possibility of future upside does not justify entry. The fund will resolve this by using prospective independent periods, destination and publication data, and 15-minute execution, without retuning this calendar. Assumptions include fees of 25 basis points per leg and slippage of 5-15 basis points, with facts sourced from https://docs.alpaca.markets/us/docs/crypto-fees, https://www.circle.com/transparency, https://www.usdc.com/, and https://runway.adityaperswal.workers.dev/data/crypto-bars.
A trading fund manager made some simulations to test how well a trading strategy works. The manager used real data from March 1 for Bitcoin, Ethereum, and Solana. The strategy was to buy and sell at certain times based on the prices of these cryptocurrencies. The manager found that the strategy did not work well, with losses of 42.24% for Bitcoin, 11.31% for Ethereum, and 21.93% for Solana. The manager also found that the strategy was sensitive to changes in the market and that more data is needed to make good decisions. The manager used a technique called resampling to test the strategy, which involves repeating the simulation many times to see how well it works. The results were not good, with high losses and low gains. The manager concluded that more data is needed to make good decisions and that the strategy should not be used without more testing. The manager used data from two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the report that on September 8, the daily Relative Strength Index, a measure of how quickly a stock or cryptocurrency price is changing, for Bitcoin, Ethereum, and Solana was 16.5270, 40.9920, and 36.2477, respectively. All of these were above the entry level of 10, while the price was above the Simple Moving Average of 100. The hourly Relative Strength Index, which measures the same thing over a shorter time period, was 45.5215, 50.3215, and 48.7828, all above the entry level of 40. There was no signal at the frozen completed cutoff. The daily trend fraction for 2026 was 26.2948, 25.0996, and 15.5378 percent. The number of oversold days was 38, 30, and 36, but the number of joint days was 6, 1, and 1. The overlapping consecutive days reduced the completed trades to 3, 1, and 1. The hourly joint bars reduced to 4, 5, and 3 trades. The controls without trend showed daily out-of-sample returns of -12.5166, -16.5445, and 14.1017 percent for Bitcoin, Ethereum, and Solana, respectively. The hourly returns were -28.2809, -31.9717, and -11.1073 percent. The Solana daily control had 20 trades, with a 60 percent win rate, a mean return of 0.8375 percent, and a confidence interval of -1.7811 to 3.3112 percent. The 15 basis points slip return was 9.6277 percent, but it failed to meet the criteria for the control winner. The trend agreement is not independent evidence of an edge. The data came from https://runway.adityaperswal.workers.dev/data/crypto-bars, and the observations were from 17457 to 17544 and 17595 to 17600.
A study was done on indicator regression. The study had three main points. First, a model called FitOLS2025 was used on 121 non-overlapping 3-day periods to predict the next open net returns for major cryptocurrencies. The model used several factors, including the previous close price, a 100-day moving average trend, and a measure called RSI2, which is a type of momentum indicator that measures the speed of price changes, and it looked at how these factors interacted with each other. Second, the model was tested on 83 separate 3-day periods in 2026. The results showed that the model's predictions were not very accurate, with mean errors of -0.7529%, -1.2662%, and -0.6976% for BTC, ETH, and SOL, respectively, compared to actual errors of -0.5905%, -0.5998%, and -0.5918%. The model also identified some joint signals, but these were not strong enough to be useful. Third, the study found that the model's positive interaction coefficients, which measure how the factors interact with each other, were 1.3783%, 2.7093%, and 2.3312% for BTC, ETH, and SOL, respectively. However, these coefficients were not enough to validate the model's predictions because the model was not very good at predicting the data it was tested on. The study concluded that the model mainly described a negative unconditional net drift, which means that the model was not very good at predicting the overall direction of the market. The study also noted that the results may not be statistically significant and that different alignments of the 3-day grid could alter the results. Additionally, the study found that the model's predictions were affected by calendar and regime dependence, which means that the model's performance varied depending on the time of year and the overall market conditions. The study also considered the possibility that the model may be beaten by simply forecasting zero returns due to forecasting costs and negative base drift, but this would not provide a useful edge for making trades. All of the figures and coefficients from the study are recorded in the observations and can be found in the files research/regression.py/json. The data for the study came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about SOL 1Hour trend-pullback holdout having insufficient independent trades. The note starts with a frozen note1384 before fetching. It mentions that the daily exponentially weighted moving average, also known as ewm, with a Wilder alpha and an RSI2 of less than 10, and the close being greater than the SMA100, and the hourly diagnostic RSI48 of less than 40, and the close being greater than the SMA2400, are distinct sampling methods and not exact daily replication. The next open entry and exit will be the next open after the RSI is greater than 60 daily or greater than 50 hourly, or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was in September 2024, the training period was in 2025, and the out-of-sample, or OOS, period was from January 1 to September 8, 2026, which was held out for this rule but reused the calendar from prior research, which is not pristine independent confirmation. The fees for entry and exit are 0.0025 and 0.9975, exactly reproducing the credited asset 25 basis points per leg, with slippage assumptions of 5 basis points per leg and 15 basis points stress, and no historical depth. The portfolio is an isolated 100 cash fully invested portfolio. The cash profit and loss is divided by the entry notional plus the entry fee. All 144 compounded payoffs reconcile to 1e-7 percentage points. The baseline data includes the symbol SOL, a 1-hour time frame, an out-of-sample period, a threshold of 40, a trend filter of true, a slip in basis points of 5, a return percentage of 2.0571309885728795, a drawdown percentage of 5.428983945916399, a number of trades of 3, a win percentage of 66.66666666666666, an average win percentage of 2.928592480689437, an average loss percentage of 3.6656875267508515, an expectancy percentage of 0.7304991448760073, a lower confidence interval percentage of -3.6656875267508515, an upper confidence interval percentage of 3.415400829960636, a quarter Kelly of 0.062359234828059454, and held missing of 0. The training return was -23.49450244882479 percent, with an N of 10 and an expectancy of -2.566572205239217 percent, and held missing of 6. The stress out-of-sample return was 1.446620903042998 percent. The quality data includes the symbol SOL, a 1-hour time frame, 17705 rows, 17712 expected, 7 missing, 0 duplicates, a first date of 2024-09-01 00:00:00+00:00, and a last date of 2026-09-08 23:00:00+00:00. There are no baseline out-of-sample invested gaps, and forward fill for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, with N1 bounds degenerate and not population uncertainty. All baseline N is less than 30, and all positive baseline confidence intervals include zero. There is no 15-minute stop validation, and parameter controls are diagnostics, with no winning variant selected. The full 144 rows of observations are from 17457 to 17544. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about SOL 1Day trend-pullback holdout having insufficient independent trades. The note starts with a frozen note1384 before fetching. The daily Wilder alpha, which is a type of average, RSI2 is less than 10 and the close is greater than SMA100, which is a simple moving average. The hourly diagnostic RSI48 is less than 40 and the close is greater than SMA2400, this is distinct sampling and not an exact daily replication. The next open entry and exit will be the next open after RSI is greater than 60 daily and greater than 50 hourly or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was September 2024, training was in 2025, and the out-of-sample period was from January 1 to September 8, 2026, which was held out for this rule but reused the calendar from prior research, which is not pristine independent confirmation. The fees for entry and exit are 0.0025 and 0.9975, which exactly reproduce the credited asset 25 basis points per leg, with a slippage of 5 basis points per leg and 15 basis points stress assumptions, not based on historical depth. The portfolio is an isolated 100 cash fully invested portfolio. The cash profit and loss is divided by the entry notional plus the entry fee, and all 144 compounded payoffs reconcile to 1e-7 percentage point. The baseline results show a return of -3.445115910082066 percent, a drawdown of 3.445115910082064 percent, and an expectancy of -3.4451159100820625 percent. The training return was -18.19790734435226 percent, with an expectancy of -2.5331341375826337 percent. The stress out-of-sample return was -3.638032905953807 percent. The quality of the data shows 738 rows, with no missing or duplicate data, and the first and last dates are September 1, 2024, and September 8, 2026. There are no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, but the N1 bounds degenerate and do not show population uncertainty. All baseline N is less than 30, and all positive baseline confidence intervals include zero. There is no 15-minute stop validation, and parameter controls are diagnostics, with no winning variant selected. The full 144 rows of observations are from 17457 to 17544. 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 headline about an automated trading fund manager's notes. The manager's system had a daily RSI, or Relative Strength Index, which is a measure of how fast a stock or cryptocurrency's price is changing, below 10 and the closing price was above the Simple Moving Average of 100. The hourly diagnostic RSI was below 40 and the closing price was above the Simple Moving Average of 2400. The next open entry would be when the RSI was above 60 daily or 50 hourly, or after 3 days or 72 hours, and there would be a forced liquidation at the last open. The system was warmed up in September 2024, trained in 2025, and the out-of-sample period was from January 1 to September 8, 2026. However, the calendar was reused from prior research, which is not a pristine independent confirmation. The fees for entry and exit were 0.0025 and 0.9975, and the slippage assumptions were 5 basis points per leg and 15 basis points stress. The portfolio was fully invested with 100 cash and the return was calculated as the cash profit and loss divided by the entry notional plus the entry fee. The baseline results were as follows: the symbol was ETH, the time frame was 1 hour, the period was out-of-sample, the threshold was 40, the trend filter was true, the slippage was 5 basis points, the return percentage was 4.4865, the drawdown percentage was 5.6646, the number of trades was 5, the win percentage was 80, the average win percentage was 1.8956, the average loss percentage was 3.0733, the expectancy percentage was 0.9018, the lower confidence interval percentage was -1.1393, the higher confidence interval percentage was 2.0648, and the quarter Kelly percentage was 0.1189. The training return was -17.0122 percent, the number of trades was 9, and the expectancy was -1.9607 percent. The out-of-sample return was 3.4468 percent. The data quality was as follows: the symbol was ETH, the time frame was 1 hour, the number of rows was 17710, the expected number of rows was 17712, the number of missing rows was 2, the number of duplicates was 0, the first date was September 1, 2024, and the last date was September 8, 2026. There were no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, and the N1 bounds were degenerate, not population uncertainty. All baseline N was less than 30, and all positive baseline confidence intervals included zero. There was no 15-minute stop validation, and the parameter controls were diagnostics, with no winning variant selected. The full 144 rows of observations were from 17457 to 17544. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'ETH 1Day trend-pullback holdout: insufficient independent trades'. This is a note from an automated trading fund manager. The manager uses a measure called the Relative Strength Index, or RSI, which is a way to gauge the speed and change of price movements. The manager also uses a Simple Moving Average, or SMA, which is the average price of a stock over a certain period of time. The manager's system has rules, such as: if the daily RSI is less than 10 and the close is greater than the SMA100, then do something. The system also looks at hourly data. The next open entry, or time to buy, is when the RSI is greater than 60 daily or greater than 50 hourly, or after 3 days. If the last open is still held after 3 days, the system will liquidate, or sell, at the next open. The system was trained on data from 2025 and tested on data from January 1 to September 8, 2026. The test was not completely independent because it reused calendar data from prior research. The system has fees, which are 0.025% to enter and 0.025% to exit. The system also assumes a slippage, or difference between expected and actual price, of 5 basis points per leg. A basis point is one hundredth of a percent. The system's performance is measured by its return, which is -3.758260817716291%. The system's drawdown, or largest loss, is also -3.7582608177162857%. The system has a win percentage of 0% and an average win percentage of 0%. The system's expectancy, or expected return, is -3.7582608177162897%. The system's quality is measured by its data, which has 738 rows and no missing or duplicate values. The data starts on September 1, 2024, and ends on September 8, 2026. The system uses data from two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about an automated trading fund manager's notes. The manager's system uses a type of moving average called an exponential weighted moving average, or ewm, with a Wilder alpha, and a measure of stock speed called RSI, or Relative Strength Index. The system had rules, such as when the daily RSI was less than 10 and the close was greater than the Simple Moving Average, or SMA, of 100, and when the hourly RSI was less than 40 and the close was greater than the SMA of 2400. The system would enter a trade at the next open and exit at the next open after the RSI was greater than 60 daily or 50 hourly, or after 3 days or 72 hours, with a forced liquidation at the last open. The system was trained from 2025 and tested from January 1 to September 8, 2026, but the test was not completely independent because it reused a calendar from prior research. The system had fees of 0.025 for entry and exit, and it assumed a slippage of 5 basis points per leg and a stress assumption of 15 basis points. The system was tested on a portfolio of 100 cash that was fully invested, and the profit and loss was calculated as the cash profit and loss divided by the entry notional plus the entry fee. The system had a baseline return of 0.627 percent and a drawdown of 3.659 percent, with 4 trades, a win percentage of 50 percent, an average win percentage of 1.889 percent, and an average loss percentage of 1.547 percent. The system also had an expectancy of 0.171 percent and a quarter Kelly criterion of 0.0227. The training return was 2.786 percent, and the out-of-sample return was -0.175 percent. The data quality was checked, and there were 2 missing rows out of 17712 expected rows, with no duplicates, and the data ranged from September 1, 2024, to September 8, 2026. The system used a bootstrap method with 5000 independent and identically distributed trades, but the results were not reliable due to degenerate bounds. The system also had no 15-minute stop validation, and the parameter controls were only diagnostics, with no winning variant selected. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the note that the daily trend for Bitcoin had a pullback holdout that was insufficient due to a lack of independent trades. The note was frozen before fetching and stated that the daily RSI, which is a measure of how quickly the price of a stock or cryptocurrency is changing, was less than 10, and the close was greater than the SMA100, which is a type of moving average that shows the average price over a certain period of time. The hourly diagnostic RSI was less than 40, and the close was greater than the SMA2400, which is another type of moving average. The next open entry would be exited at the next open after the RSI was greater than 60 daily or 50 hourly, or after 3 days or 72 hours, with a forced last open liquidation. The warm-up period was from September 2024, and the training period was from 2025. The out-of-sample period, which is a period of time used to test the performance of a model, was from January 1 to September 8, 2026, and was held out for this rule, but was reused from prior research, which is not pristine independent confirmation. The fees for entry and exit were 0.025 and 0.9975, respectively, and were exactly reproduced with a credited asset of 25 basis points per leg, with slippage assumptions of 5 basis points per leg and 15 basis points stress, and no historical depth. The portfolio was isolated with 100 cash and was fully invested. The cash PnL, which is the profit and loss of the portfolio, was divided by the entry notional plus the entry fee, and all 144 compounded payoffs reconciled to 1e-7 percentage points. The baseline results were as follows: the symbol was BTC, the time frame was 1 day, the period was out-of-sample, the threshold was 10, the trend filter was true, the slip in basis points was 5, the return percentage was 0.18177174799714635, the drawdown percentage was 1.8565435856762624, the number of trades was 3, the win percentage was 66.66666666666666, the average win percentage was 0.7451625511902433, the average loss percentage was 1.290625790259734, the expectancy percentage was 0.06656643737358425, the confidence interval low percentage was -1.290625790259734, the confidence interval high percentage was 1.3954415537106202, the quarter Kelly was 0.02233285786680303, and the held missing was 0. The training return was 2.3869241956420666%, the number of trades was 7, the expectancy was 0.36453314982556506%, and the held missing was 0. The stress out-of-sample return was -0.4175198590269105%. The quality of the data was as follows: the symbol was BTC, the time frame was 1 day, the number of rows was 738, the expected number of rows was 738, the number of missing rows was 0, the number of duplicates was 0, the first date was September 1, 2024, and the last date was September 8, 2026. There were no baseline out-of-sample invested gaps, and forward filling for marking and indicators may distort the regime. The SOL hourly train held 6 missing intervals. A bootstrap of 5000 independent and identically distributed trades was performed, and the N1 bounds were degenerate, not population uncertainty. All baseline N was less than 30, and all positive baseline confidence intervals included zero. There was no 15-minute stop validation, and parameter controls were diagnostics, with no winning variant selected. The full 144 rows of observations were from 17457 to 17544. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A review of the 06UTC flow and recovery was conducted. The premises of the review are as follows: 1. Circle's net 7-day issuance of USDC was $0.94 billion as of September 7, but the balance on USDC.com was $74.43675568681 billion at 06:01 on September 9, which is below the $74.47079165978 billion balance on September 8 and different from the $74.44261970278 billion balance at 05:30 on the same day. This data is treated as a stock snapshot, not a separately timestamped net mint event. 2. The number of ETF shares on September 8 was unchanged, and the actual CSV quantities are still from September 4, so the timing of incremental spot demand is unproven. 3. Liquid explorers remain 897 blocks apart and stale, with an unchanged operator statement that describes preparation, not verified authorized payouts. The conclusion is that there is no flow or recovery trade trigger. This conclusion is based on the absence of evidence, not proof that flows or recovery cannot occur. A strong counterargument is that issuance and restart may precede publication, and prospective monitoring can reveal it, but reconstructed dates cannot justify anticipatory historical entries. The sources of the observations are numbers 17414-17416 and ETF observations, which can be found at https://www.circle.com/transparency, https://www.usdc.com/, https://www.ishares.com/us/products/333011/ishares-bitcoin-trust-etf, https://www.ishares.com/us/products/337614/ishares-ethereum-trust-etf, https://blockstream.info/liquid/api/blocks, https://liquid.network/api/blocks, and https://x.com/Blockstream. There are no holdings to review.
At 04UTC, the decision was made that issuance is not a validated buying signal, so the $235.86 in cash will be retained. The starting state was $850.99 in equity, $235.86 in cash from crypto, with no open or queued lots, and only own trade 10, which was operator-liquidated with a raw return of -0.08099%. The goal is to achieve reproducible net profit with a positive entry-conditioned payoff and a known publication time and 15-minute execution. The premises for this decision were: 1) Circle's net USDC is increasing by $0.94 billion per week, supporting more outstanding dollars, but gross minting is mostly offset, and destinations are unidentified. 2) Tazapay is an existing payment system with a $400 million stock consideration and an expected close in 2027, which does not immediately affect USDC or spot purchases. 3) New frozen USDC rules and diagnostics all lose, with daily holdout BTC/ETH/SOL losses of -19.3665%, -26.6564%, and -25.9190%, and hourly failures and held gaps invalidate execution. 4) Cash basis analysis shows average wins and losses, but frozen regression fails with a zero forecast, and simulated diversification does not repair the negative payoff. The conclusion is that there will be no orders or proposals, and the $235.86 will remain in cash. The validity of this decision is based on the mandate requiring an edge, which is absent here. The soundness of this decision is limited by reconstructed historical data, small samples, and unknown routing. A strong counterargument is that destination-specific exchange flows might work, but this would require a separately preregistered prospective first-seen rule with a positive costed lower bound and valid daily, hourly, and 15-minute fills. The decision is not to retune this failed family of rules. The issuer, distributors, and payment users benefit first, but eventual major-token benefit requires incremental buying and fees. Monitoring is complete, and the next job is scheduled for 06UTC.
A review of Monitor105 was conducted. The IBIT and ETHA webpages still showed 1,387,560,000 and 467,680,000 shares as of September 8. However, the actual holdings CSVs were still from September 4 and no spot purchases from September 8 could be located. Circle's transparency report was still from September 7, showing a net balance of $0.94 billion. USDC.com reported a new stock of $74.47079 billion as of September 8, which adds coverage but is not from the same cutoff flow. Liquid tips 4051232 and 4050335 remained 897 apart and 47.35 and 62.30 hours old at 04:10. The operator's parsed statement remained unchanged in preparation. A recipient had 598.50007353 BTC and a reserve of 3597.47201026, but this did not establish an authorized payout, forced sale, or peg out restoration. Actual job 117 was completed at 03:30 with a numeric payload confirmed. Old job 114 was ended at 04:01, cancelled, and replaced with job 121. New quotes and funding requests were made through 06:01 for jobs 120, 121, and 122, with a request to preserve 14 and 25. The conclusion is that the prior positive issuance context persists, with no operational new trigger. The sources used were https://www.ishares.com/us/products/333011/ishares-bitcoin-trust-etf, https://www.ishares.com/us/products/337614/ishares-ethereum-trust-etf, https://www.circle.com/transparency, https://www.usdc.com/, https://blockstream.info/liquid/api/blocks, https://liquid.network/api/blocks, and https://x.com/Blockstream. The same providers and snapshots are not independent new events, and zero allocation remains.
On September 8, the daily prices of Bitcoin, Ethereum, and Solana were $78,448.68, $2,484.53, and $103.36, respectively. The Simple Moving Average of 20 days was greater than the Simple Moving Average of 100 days for each of these cryptocurrencies. A technical indicator called Average True Range, which measures the volatility of an asset, was 2,271.07 for Bitcoin, 88.76 for Ethereum, and 5.62 for Solana, representing 2.90%, 3.57%, and 5.44% of their respective closing prices. These values can be used as a reference for the possible range of prices, but they are not a reliable indicator for selling. At 04:10, the spread between the prices of Bitcoin, Ethereum, and Solana was 2.95, 3.36, and 12.06 basis points, and the yield recovery hurdles were 0.5315%, 0.5357%, and 0.6231%. The highest price at which someone was willing to sell was $79.01, $82.50, and $98.99, which is below the maximum price of $212.75. The cash available for trading was $235.86, and there were no holdings. The weekly payoffs for a trading rule based on the USDC were negative, and the momentum indicators also showed losses of 13.49%, 26.53%, and 29.59% on a daily basis, and 13.10%, 24.69%, and 32.36% on an hourly basis. This means that the indicators did not show a positive edge. The risk budget was 8.51, and the quarter Kelly criterion, which is a measure of the optimal size of a trade, was zero. The sources of this data include crypto-bars, crypto-snapshots, and crypto-fees from Alpaca Markets, as well as query trades from a fund crypto dataset and technical observations from 17401, 17404-17406, and technical.json.
A test was done to see if changing the rules for buying and selling would improve results. This test, which is called a counterfactual and not a proposal for changing the size of trades, looked at what would happen if the rules for trading with USDC were applied to trades made in 2026. The test used 10,000 different paths, each with 10 cycles, and started with $235.86, with $78.62 in each major trade. The test also took into account fees and the difference between the price at which a trade was made and the price at which it was executed, which is called slippage. The average result of these trades was a loss of 3.21270%. The test also looked at how the prices of different cryptocurrencies, including BTC, ETH, and SOL, were related to each other. The results showed that the prices of these cryptocurrencies were highly correlated, with correlation coefficients of 0.91259 for BTC and ETH, 0.85103 for BTC and SOL, and 0.88099 for ETH and SOL. The test also found that the ending values of the trades were $125.56 at the 5th percentile, $168.44 at the 50th percentile, and $222.01 at the 95th percentile, and that the frequency of losses was 98.28%. The median maximum drawdown was 30.43% and the 95th percentile maximum drawdown was 47.46%. The test used only 8 empirical vectors and did not take into account the fact that the results may be different over time, which is called serial dependence, or the fact that the market may change, which is called regime change, or the risk of extreme events, which is called tail risk. The test also did not use stops or targets, and trades were executed every 15 minutes. The idea that trading in three different cryptocurrencies would reduce risk is not strong, and the results of the test show that the outcomes of the trades are still highly dependent on each other. The Quarter Kelly criterion, which is a measure of the optimal fraction of wealth to invest, was zero because the individual means were negative, so no dollars were approved for investment. The simulation shows what would happen if the rules for overriding the gate, which is a mechanism for controlling risk, were not followed, and it does not take into account the probability of future losses. The sources used for this test include https://stablecoins.llama.fi/stablecoin/2 and https://runway.adityaperswal.workers.dev/data/crypto-bars, and the files research/audit_sim.py and json.
An independent audit found a difference in how returns were reported, but not in the actual portfolio returns. The issue was that the trades' returns were calculated using the entry amount as the denominator, while the total equity was calculated including fees. To correctly calculate the cash return, the formula is: profit and loss divided by the size of the trade multiplied by the entry price plus the entry fee, which is equivalent to dividing the earlier trade returns by 1.0025. After correcting this, the compounded cash returns for all six baseline holdout periods now match the portfolio returns within 1e-8 percentage point. As a result, the average wins, losses, and expectancy, as well as the bootstrap confidence interval in rows 1460-1465, should use the corrected observations. However, the return, drawdown, N, win rate, and quarter Kelly remain unchanged. The daily expected payoffs for BTC, ETH, and SOL were corrected to -2.581506%, -3.613642%, and -3.442937% respectively, and the hourly expected payoffs were corrected to -2.609239%, -3.451847%, and -3.175189% respectively. A news scan found the headline about rejection remains. The files research/test_supply.py and backtests_cash_basis.json were updated to reflect the cash basis, and audit_sim.py was used for verification. The raw price and source data can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars, and the supply data can be found at https://stablecoins.llama.fi/stablecoin/2. No conversion of invalid hourly execution into trade approval was made.
A news scan found the report that the USDC flow regression is unstable and fails frozen forecasting. The report includes the following points: 1. A preregistered weekly ordinary least squares regression, which is a statistical method to analyze the relationship between variables, (notes1343/1348) was conducted from Monday close to next Monday close return on prior 7-day USDC growth lag 1 day, USDT growth lag 1 day, and own prior 7-day return. The results are based on 51 non-overlapping 2025 development outcomes and 35 in 2026 holdout through September 8. The analysis used HC3 errors, which is a type of statistical error correction, and did not include vintage publication clock, macro controls, or serial-HAC correction, which are methods to account for various types of errors. 2. The USDC coefficients for BTC, ETH, and SOL, which are types of cryptocurrencies, flipped from -0.161, -0.538, and -0.330 in development to +0.747, +1.018, and +1.300 in holdout. The holdout 95% confidence interval, which is a measure of the reliability of the results, is [-0.765, 2.259], [-1.981, 4.016], and [-0.897, 3.496]. The units return fraction per supply-growth fraction, which means the results are measured as a fraction of the total supply. 3. The frozen 2025 forecast holdout correlations are -0.0155, -0.0280, and -0.0559. The mean squared error, which is a measure of the average error, is 0.004334, 0.009438, and 0.009716, which is worse than a zero forecast of 0.004254, 0.008319, and 0.009190. 4. The supply growth also correlates with already elapsed returns, which means that the growth in supply is related to past returns. However, simultaneity and omitted risk factors, which are statistical issues, preclude causation, which means that we cannot conclude that supply growth causes returns. The valid conclusion is that there is no reliable forecast in this sample, but this does not prove that supply never matters. A strong counterexample is that a destination-specific flow could work despite the failure of the aggregate flow. The report requires prospective first-seen records and untouched outcomes. The source of the data is https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://runway.adityaperswal.workers.dev/data/crypto-bars. The files research/test_supply.py, regression.json, and weeklyCSVs were used in the analysis. The calendar holdout was reused by prior strategies, and the allocation is 0.
A news scan found the notes from an automated trading fund manager. The manager used a strategy to buy and sell based on the supply of USDC, a type of stablecoin, which is a digital currency that is meant to be worth the same as a traditional currency. The strategy was to buy when the supply of USDC on Sunday was higher than it was seven days earlier, and to decide whether to do this on Mondays at 01UTC at the market open, and to sell at the next weekly period when the condition was negative or zero. The strategy started with no position and would sell everything at the end of the period if it had not already sold. This strategy was tested from January 1, 2026, to September 8, 2026. The test took into account a one-day delay in getting the supply data and also looked at how sensitive the results were to a two-day delay. It also accounted for fees of 25 basis points per trade and slippage of 10 to 30 basis points. The Vectorbt program was used to generate signals to buy and sell, but it did not use stop-loss orders, target prices, or monitor the market every 15 minutes or simulate the depth of the market. The historical supply data was not complete, so the results are only for diagnostic purposes, even if they showed a profit. The return on investment during the development period was -26.7922%, and during the holdout period it was -23.8999%, with a maximum drawdown of 31.1458% and 8 trades, of which 25% were winners, with an average win of 4.6175% and an average loss of 5.7833%, resulting in an expectancy of -3.1831% and a quarter Kelly criterion of 0. The stress return was -26.2966%. A bootstrap simulation with 10,000 draws and a seed of 2026090904 showed a mean return with a 95% confidence interval of [-7.255080666634638, 0.9747630964604378]%, but this was not a reliable forecast due to the small and correlated sample. One hour of data was missing during the holdout period while the strategy was invested, and the strategy was also exposed to stale bar data and did not use 15-minute exits, which disqualifies it from being executed. All 8 scenarios that were tested, which looked at different costs, lags, and time windows, resulted in losses. The sources of the data were https://stablecoins.llama.fi/stablecoin/2, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://docs.alpaca.markets/us/docs/crypto-fees. The files research/test_supply.py, backtests.json, and tradeCSVs were used. The deployment of the strategy was rejected because it did not show any edge, or advantage, from price momentum or the geography of USDC.
A trading strategy based on the USDC supply was tested. The strategy involved going long on Sundays when the USDC supply was higher than it was seven days earlier, and exiting on Mondays at 00UTC when the condition turned negative or zero. The test ran from January 1, 2026, to September 8, 2026. The results showed a development baseline return of -27.6095% and a holdout return of -25.9190%. The strategy had a drawdown of 31.4193%, with 8 wins out of 25 trades, and an average win of 4.8545% and an average loss of 6.2202%. The expectancy was -3.4515%. A stress test return was -28.2521%. A bootstrap simulation with 10,000 draws showed a mean return of -8.2217% to 1.1434%. However, the sample was small and correlated, so the results are not forecast confident. All 8 cost, lag, and window scenarios tested lost money. The sources used for the test included stablecoin data from llama.fi, crypto bars from runway.adityaperswal.workers.dev, and crypto fees from alpaca.markets. The test was run using the research/test_supply.py file and the results were recorded in the backtests.json and tradeCSVs files. The strategy was rejected for deployment because it did not show an edge in transferring price momentum or USDC geography.
A trading strategy for Ethereum (ETH) was tested. The strategy involved buying ETH when the USDC supply on Sundays was higher than it was seven days earlier. The decision to buy was made on Mondays at 01UTC and the position was closed when the condition turned negative or zero. The test started from a flat position and ended with liquidation at the last period's open. The test was run from January 1, 2026, to September 8, 2026. The results showed a development baseline return of -27.4972% and a holdout return of -25.3644%. The strategy had a drawdown of 28.6337%, a win rate of 37.5%, an average win of 1.5716%, and an average loss of 6.4797%. The expectancy was -3.4605%. A stress test return was -27.7149%. The results were not promising, with all eight cost, lag, and window scenarios resulting in losses. The strategy was tested using historical supply data, but the data lacked original publication vintages, making the results diagnostic only. The test also found that one missing holdout hour occurred while invested, and stale-bar exposure and absent 15-minute exits disqualified execution. The sources used for the test included stablecoin data from llama.fi, crypto bars data from runway.adityaperswal.workers.dev, and crypto fees data from Alpaca Markets. The files used for the test were research/test_supply.py, backtests.json, and tradeCSVs. The strategy was rejected for deployment due to no edge transfer from price momentum or USDC geography.