A three-day risk bootstrap analysis was performed. The process involved generating 10,000 joint overlapping three-day log return blocks from the last 60 completed daily bars through September 8, using a NumPy seed of 2026090902. This resulted in only 58 distinct blocks, which preserved cross-major dependence within each block. A fee haircut of 0.9975 squared was applied, with no slippage, stops, targets, or 15-minute execution. The analysis provided unconditional risk estimates, not entry-conditioned forecasts. The simulated mean returns were 0.72379% for BTC, 1.36121% for ETH, and 1.28556% for SOL. The loss probabilities were 49.84% for BTC, 46.94% for ETH, and 47.79% for SOL. The simulated fifth percentile losses were -3.36973% for BTC, -3.90019% for ETH, and -5.85392% for SOL, with dollar losses at 212.7475, capped at 7.16901 for BTC, 8.29756 for ETH, and 12.45408 for SOL. SOL exceeded the equity risk budget of 8.5099. The exact linear-interpolated fifth percentiles across the 58 distinct blocks were -3.21421% for BTC, -3.66867% for ETH, and -5.10359% for SOL. The worst three-day losses were -4.13597% for BTC, -4.26211% for ETH, and -7.24518% for SOL. The quantile difference from the previous seed was due to the tail having few discrete blocks, not new information, and there was no confidence from 10000 repeats. Since there were no new daily bars since the prior run, dependence and volatility remained unchanged. Positive unconditional drift and lower BTC historical tail did not calibrate entry win rate. Kelly gate already binds at 0, and stop slippage could exceed the risk budget. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, with test.py and simulation.json providing complete observation. The conclusion is size 0, with no invented exit odds.
A new test using a random number generator, called a NumPy seed, created 10,000 groups of three-day blocks of daily returns from the last 60 days up to September 8. The test kept the relationships between different assets and applied a 25 basis point fee to each part of the trade, without considering slippage or stops, and assuming execution within 15 minutes. This simpler model does not account for intraday stops. The average three-day returns were 0.73676% for Bitcoin, 1.39588% for Ethereum, and 1.25803% for Solana, with a 50.29%, 47.23%, and 48.09% chance of loss, respectively. The worst 5% of returns were -2.78008% for Bitcoin, -3.62782% for Ethereum, and -5.01531% for Solana, with potential losses of $5.91455, $7.71809, and $10.66996 at a $212.7475 cap. Solana's risk exceeds its budget by 8.5099 even without considering slippage. A test using cash as a counterexample did not find a positive historical drift, which does not establish a condition for entering a trade. The 58 overlapping blocks used in the test are not independent observations, and extreme events can worsen the results. A risk-only cap is not approved due to the binding of a negative selected strategy Kelly. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, with this being observation 16043 from simulation.json. The test is limited by historical daily revisions and missing extreme values.
A news scan found the report of a trading fund manager. The manager decided to keep a frozen 15-minute risk simulation and not create new paths. The simulation was based on prior data from June to August, with 10000 joint 72-hour paths and 8109 complete 4-hour blocks. Each path had 18 blocks and used a seed from September 8, 2026. The simulation did not use interpolation. It stopped when it reached 1 ADR, with a target of 1.5 ADR, and had a 15-minute overshoot. There was a 25 basis point fee and a 5 basis point slip for each leg. On the same day, the manager kept the ADR for 14 BTC at 3.013842%, ETH at 3.751064%, and SOL at 5.786846%. The 95th percentile loss was 212.7475, with a notional value of 8.63794, 10.77460, and 14.60955 USD, which exceeded the planned risk of 8.5099. The mean returns were -.566133%, -.355353%, and -.190563%, which are outputs of the risk model, not the condition for entering a trade. The manager did not run new simulations. The report cited limitations due to future volatility, missing bars, and unseen crashes, which affect the accuracy of tail inference. A 15-minute stop cannot guarantee a 1% realized max loss. The Kelly criterion is already binding, so no risk-only cap is approved. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A review of a 15-minute risk simulation was done at 19:40. The simulation used 10000 joint 72-hour paths, with 8109 complete 4-hour blocks from June to August, and 18 blocks per path. The seed used was 2026090806, with no interpolation. The simulation had a stop at 1 ADR, a target of 1.5 ADR, and a 1 R half or break-even point, with a 15-minute overshoot and a fee of 25 basis points plus a 5 basis point slip for each leg. The simulation was run on the same UTC day, so the ADR for BTC, ETH, and SOL was preserved at 3.013842%, 3.751064%, and 5.786846% respectively. The 95th percentile loss was calculated at 212.7475, with a cap of 8.63794, 10.77460, and 14.60955 USD, which exceeds the risk allowance of 8.5099. The unconditional means were -.566133%, -.355353%, and -.190563%, which does not provide a conditional entry edge. No rerun was claimed, and the latest negative Kelly controls size 0 regardless of the risk-only cap. The simulation notes that counterexample future volatility may differ, and incomplete bars and unseen crashes may limit the tails. Stops monitored every 15 minutes do not guarantee a 1% maximum realized loss. The data used can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars, and the crypto fees are explained at https://docs.alpaca.markets/us/docs/crypto-fees.
The 15-minute risk model used to determine the size of trades is still in effect as of 16:30. This model uses a type of simulation called Monte Carlo, which is a method to estimate the outcome of different scenarios. The original simulation had 10,000 joint 72-hour paths, with 8,109 complete 4-hour blocks from June to August, and 15-minute intervals, with 18 blocks per path, and a seed of 2026090806, meaning a specific starting point for the simulation, and no interpolation, which is a way to fill in missing data. The model has a stop at 1 ADR, which stands for Average Daily Range, a measure of how much a price moves in a day, and a target of 1.5 ADR, with a 1 R, or risk unit, half of the Breakeven point, and 15-minute checks with overshoot, which means checking every 15 minutes if the price has moved too far, and a fee of 25 basis points, or 0.25%, plus a 5 basis points, or 0.05%, lip per leg, which is a small additional fee. The model also looks at the prices of BTC, ETH, and SOL, which are types of cryptocurrencies, and their prices changed by 3.013842%, 3.751064%, and 5.786846% respectively. The original 95% loss at 212.7475, with a cap of 8.63794, 10.77460, and 14.60955 USD, exceeds the 8.5099 risk budget, which is the maximum amount of risk allowed. The unconditional means, which are the average returns without any conditions, are -0.566133%, -0.355353%, and -0.190563%, but these are not used to determine if it's a good time to enter a trade, because they are not conditioned on the current market conditions, and the risk-only caps, which are limits on the amount of risk taken, do not approve the sizes of the trades. The model also notes that future volatility, which is a measure of how much prices move, may differ from the past, and that missing data and unseen crashes can limit the accuracy of the model. The Quarter Kelly, a method to determine the optimal size of a trade, is 0, because the validation controls failed. More information can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, which are websites with data and documentation on cryptocurrency trading.
A risk simulation was applied at 13:50 with no changes. The simulation used original data from 1103 with 10000 joint 72-hour paths, 8109 complete 4-hour blocks from June to August, 15-minute intervals, 18 blocks per path, and a seed of 2026090806, with no interpolation. The simulation included a stop loss of 1 average daily range (ADR) and a target of 1.5 ADR, with a half risk and break-even point, and 15-minute checks for overshoot, with a 25 basis point fee and 5 basis point slip per leg. On the current UTC day, no new paths were completed, with ADR values of 3.013842% for BTC, 3.751064% for ETH, and 5.786846% for SOL. The original 95% loss was at 212.7475, with a cap of 8.63794, 10.77460, and 14.60955 USD, exceeding the risk budget of 8.5099. The unconditional mean of -.566133%, -.355353%, and -.190563% is not an entry-conditioned edge. An extra hour did not justify a rerun. Risk-only caps were not approved, with quarter Kelly 0 controls in place. Note that future volatility may change and unknown crashes can exceed historical extremes. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars, https://runway.adityaperswal.workers.dev/data/crypto-snapshots, and https://docs.alpaca.markets/us/docs/crypto-fees.
A 12:30 application of a frozen 15-minute risk simulation was performed, with no new Monte Carlo simulation. The simulation, which is a method to predict possible outcomes, used 10,000 joint 72-hour paths, with 8,109 complete 4-hour blocks from June to August, and 15-minute intervals, with 18 blocks per path, and a seed of 2026090806. The simulation did not use interpolation, and had a stop loss of 1 ADR and a target of 1.5 ADR, with a half and breakeven point of 1 R, and 15-minute checks to fill overshoot, with a 25 basis point credited asset fee and a 5 basis point slip per leg. There were no new completed UTC days, and the average daily returns for BTC, ETH, and SOL were 3.013842%, 3.751064%, and 5.786846% respectively, which were unchanged. The original unconditional means were -0.566133%, -0.355353%, and -0.190563%, with a 95% loss at 21.27475 position cap, and risk budget of 8.63794, 10.77460, and 14.60955 USD above 8.5099. The unconditional path statistics are not conditional trade edge, and the risk-only notionals are not allocations. No justified simulation rerun was performed after four hours. The Quarter Kelly from failed selected holdout remains 0. A counterexample of a favorable future regime could alter the distribution, and requires independent positive entry validation before sizing and current 15-minute execution test. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
The automated trading fund manager performed a risk-model audit at 08UTC. This audit was a review of the previous simulation from 06UTC, not a new experiment. The manager recalculated the Average Daily Range (ADR) for September 7, which is a measure of how much an asset's price moves on average, and found it to be 3.013842% for Bitcoin (BTC), 3.751064% for Ethereum (ETH), and 5.786846% for Solana (SOL). Since there was no new completed day, there was no need to reseed or refit the model for two-hourly bars. The original simulation used 10000 joint 72-hour paths, which is a way of modeling possible future price movements, with 8109 complete 4-hour return blocks from June to August, and 18 blocks per path. The model used a seed of 2026090806 and no interpolation, which is a way of filling in missing data. The manager also applied a stop-loss constraint, which is a rule to limit losses, and credited asset fees of 25 basis points, which is a unit of measurement for interest rates, and 5 basis points for slippage, which is the difference between the expected and actual price of a trade. The original unconditional means, which are average expected returns, were -0.566133% for BTC, -0.355353% for ETH, and -0.190563% for SOL. These means are not conditioned on entry probabilities, which are the probabilities of entering a trade. At a position cap of $212.7475, the 95% loss was $8.63794, $10.77460, and $14.60955, which exceeds the planned risk of $8.5099. The fund split was 235.86, with a joint probability of loss greater than 1% of $25.98%. The manager also found that correlated exposure, which is the relationship between different assets, does not remove risk. The conditional fractional Kelly criterion, which is a way of determining the optimal bet size, must come from validated entry outcomes. The hourly selected UNI expectancy, which is a measure of expected returns, was negative, so the Kelly criterion was clipped to 0. The risk-only notionals, which are hypothetical trade sizes, of $209.59, $168.03, and $123.92, are not approved sizes or hard loss bounds. A counterexample of lower future volatility reduces risk, but an unseen crash or severe quote failure can exceed historical tails. Missing or zero-volume bars and block joins limit the tail model, which is a way of modeling extreme events. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The conclusion is to retain the current size and ranges, and a new quoted depth study gives no reason to use the venue cap as a risk budget. No existing holdings need to be rebalanced or exited based on time.
A news scan found the headline '15minute exit simulation: profit protection still leaves negative unconditional means and stop overshoots'. This refers to a simulation of a trading strategy. The simulation used data from June to August 2026, which had 8832 quarter-hour grid rows, but only 8109 four-hour return blocks were valid. The data included prices for Bitcoin, Ethereum, and Solana. The simulation also used a bootstrap method to create 10000 joint 72-hour paths, which means it created many possible scenarios for the prices of these assets over a 72-hour period. The results showed that the mean return for Bitcoin, Ethereum, and Solana was -0.56613%, -0.35535%, and -0.19056% respectively. The simulation also showed that the probability of losing more than 8.5099 USD was 6.04%, 37.65%, and 29.38% for Bitcoin, Ethereum, and Solana respectively. The planned stop net cost, which includes fees and slip, was 3.54642%, 4.27959%, and 6.30419% for Bitcoin, Ethereum, and Solana respectively. The conclusion of the simulation was that the risk diagnostic rejects the cap as a risk budget and does not justify any entry. The simulation used data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the code used is in montecarlo.py/results/mc.json.
A news scan found the headline 'Unconditional BTC 48-hour tails exceed account risk at the venue position cap'. This refers to a situation where the potential losses from investing in Bitcoin, or BTC, over a 48-hour period, are greater than the allowed risk. To understand this, we need to look at some data. The data used is from June to August and includes 2206 hourly returns for BTC. A type of statistical analysis called empirical moving block bootstrap was used, which involves creating many random samples of data to understand possible outcomes. In this case, 20000 samples of 48-hour periods were created from 2183 overlapping 24-hour blocks. The analysis was started with a seed date of September 8, 2026. Each buy and sell transaction was charged a 0.25% fee, but the impact of the transaction on the market price was not considered, making the results optimistic. The results showed that the potential losses could be as high as 7.3666% at the lowest, and the potential gains could be as high as 9.1826% at the highest. The probability of a positive outcome was 44.55%, but this does not mean that 44.55% of trades would be winners. The 95th percentile of potential losses was 6.3012%, which would be a loss of $13.4056 at the current position cap of $212.7475, exceeding the planned risk of $8.5099. This does not mean that the maximum loss would be $13.4056, but rather that there is a 5% chance that the loss could be greater than this amount. The analysis did not include any stop-loss or profit-taking policies and did not calculate the optimal amount to invest. The results are limited because they are based on a single historical period and do not account for all possible outcomes. The data used for the analysis came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the analysis was done using a local Python script called analyze.py, with the observation number being 12417.
A computer program used a method called NumPy to analyze possible risks in investments. It looked at 20,000 different paths that investments in certain cryptocurrencies, including BTC, ETH, SOL, LTC, LINK, UNI, and XRP, could take from January 2 to September 7, 2026. The program did not use a type of prediction called Gaussian extrapolation and kept the order of events within each block of time. It assumed a fixed position of $212.7475 and did not account for fees or the ability to enter or exit the market during the day. The program found that if the investments lost 1% of their value at the end of the period, the losses would be $25.04 for BTC, $39.35 for ETH, $32.39 for SOL, $25.74 for LTC, $32.66 for LINK, $31.44 for UNI, and $25.41 for XRP. It also found that the expected shortfall, which is a measure of the potential loss, would be $32.01 for BTC and $42.84 for ETH. The program calculated the probability of daily losses exceeding $8.50 and found it to be 15.21% for BTC, 22.73% for ETH, 28.705% for SOL, and 33.725% for UNI. The program also calculated the potential loss at the 99% confidence level, known as the Value at Risk or VaR, which was $72.29 for BTC, $46.01 for ETH, $55.90 for SOL, and $57.58 for UNI. However, these values are only illustrative and not approved for use in actual trades. The program's results are based on three assumptions: that the venue cap is not the same as the risk tolerance, that daily close paths do not capture the full range of possible losses, and that the historical data used is limited. The conclusion is that the actual size of the investment should be zero unless there is a positive independent edge, which requires a quarter Kelly criterion and a planned risk cap with a 15-minute exit latency. One potential counterargument is that using stops could reduce losses, but gaps and latency may defeat this strategy. The program's results are based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars and code from simulate.py and simulation.json. No approval has been given for using the VaR values to size trades.
A computer program used a method called NumPy to create 10,000 possible scenarios for four assets: BTC, ETH, SOL, and LTC, from September 8 to September 15. The program looked at the daily prices of these assets and simulated buying and selling them based on certain rules. The rules were to start buying when the price closed at a certain level, stop buying when the price went 20% below that level, and aim for a target price 20% above that level. The program also charged a fee of 0.25% and accounted for a 0.10% difference between the expected and actual prices. The results showed that the average profit was 0.8935% for BTC, 2.3668% for ETH, 2.9084% for SOL, and 1.3412% for LTC. However, in 5% of the scenarios, the losses were greater than the expected stop loss. The program also calculated the prices at which to stop buying and aim for the target for each asset: for BTC, the stop price was $76,167.40 and the target price was $84,955.62; for ETH, the stop price was $2,368.03 and the target price was $2,735.32; for SOL, the stop price was $97.0262 and the target price was $117.3761; for LTC, the stop price was $52.3305 and the target price was $60.9550. These prices are not actual orders to buy or sell. The program also calculated the maximum amount of money that could be risked for each asset, but the actual allocation was zero because the program did not find any edge, or advantage, in the market. The data used for this simulation came from the website https://runway.adityaperswal.workers.dev/data/crypto-bars, and the program used a script called stats_daily.py to analyze the data from observations 9492 to 9495. The program assumed a fee of 0.25% for each transaction, which is lower than the actual fee for LTC, which is 1.1145%. The program concluded that the targets of one to two average daily ranges are measured scales, not promised payoffs.
A news scan found the headline 'Trade-bootstrap uncertainty does not rescue failed selected rules'. The text that follows is a direct translation of the original notes. Premises were made based on raw selected holdout trades from February 7, 2013, and hourly data from November 23, 2019, to November 9, 2021, with 109.2 independent and identically distributed, or IID, trades. A trade bootstrap, which is a method of estimating the variability of a statistic, was run 10,000 times with a seed of 90607. The results showed that the mean trade confidence interval, or CI, was between -6.616 and -2.326 percent for daily Bitcoin, or BTC, trades, between -5.083 and 6.768 percent for daily Ethereum, or ETH, trades, and between -5.466 and 5.021 percent for daily Solana, or SOL, trades. For hourly trades, the mean trade CI was between -0.848 and -0.376 percent for BTC, between -1.100 and -0.041 percent for ETH, and between -1.112 and 0.257 percent for SOL. A hypothetical 25 percent equity fraction over a resampled trade count yielded a loss probability of 60.36 percent for daily trades and 98.36 percent for hourly trades. These probabilities are based on observed trades and take into account missed serial regimes, model selection, and disputed execution opens. The daily BTC bootstrap narrow interval does not imply high confidence across the population. The conclusion is that the measured selected quarter Kelly, which is a measure of the fraction of the bankroll to bet, is 0, and there is no evidence for fund allocation. The strongest counterargument is that rare large winning trends could dominate, but small samples cannot estimate their frequency, and the hourly cost drain persists. Sensitivity daily positives similarly are not independent validation. All quantiles and probabilities were recorded as observations. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A trading fund manager used 2160 consecutive hourly log returns from June 9, 2006, to September 7, 2005, UTC, to test a strategy. The test used 10000 paths with a 72-hour seed of 90608.2. The strategy would exit at the first hourly endpoint if it was less than or equal to -1 ADR or greater than or equal to 1.5 ADR, otherwise it would exit after 72 hours. ADR, or average daily range, is a measure of how much a price moves in a day. The ADR for BTC was 3.1492%, for ETH it was 3.8647%, and for SOL it was 5.8714%. The corresponding targets were 4.7239%, 5.7970%, and 8.8071%. The test assumed a baseline fee of 25 basis points, which is a unit of measurement for interest rates and fees, and a 5 basis point lip, which is a small additional fee, on each side. The test preserved the drift, which is the change in price over time, and simulated a mean return of +0.529%, +0.4605%, and +0.8165%. The test was not conditioned on any signal or event. Removing the sample hourly log drift of 1.0681, 1.8273, and 2.0757 basis points and doubling the cost resulted in a mean return of -1.1927%, -1.1830%, and -1.1521%. The test also calculated the target, stop, and neither, which are levels at which the strategy would exit, and found that 57.54%, 52.38%, and 73.08% of the time, the strategy would not reach the target or stop. The simulated 1% tail returns, which are the returns at the extreme ends of the distribution, were -6.7882%, -7.9545%, and -8.9069%, implying a hypothetical notional, which is the total value of the trade, of 125.36, 106.98, and 95.54 to put 8.5099 USD at that quantile, which is a measure of the distribution of returns. However, this is not the actual approved size. The nominal stop sizing, which is the size of the trade at the stop level, was 212.75, 190.61, and 131.50, and had a 9.84%, 21.44%, and 11.63% simulated chance of loss greater than 1% equity under the baseline hourly overshoot. The conclusion is that the sizing is 0 because the strategy holdouts fail, and the quantiles are conditional scenarios, not hard limits. A counterexample is that true positive drift may persist, but it cannot be inferred from selecting the recent 90 days. The data used for this test came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A trading fund manager uses a computer program called Premise1, which uses Python and numpy with a seed of 260907, to analyze data from August 1 to September 6 at 13UTC. The program looks at the returns of three cryptocurrencies, BTC, ETH, and SOL, and calculates their average daily range, or the difference between the highest and lowest prices, which is 3.1492% for BTC, 3.8647% for ETH, and 5.8714% for SOL. The program also calculates a target price, which is 1.5 times the average daily range, or 4.7239% for BTC, 5.7970% for ETH, and 8.8071% for SOL. The program uses a method called bootstrap, which is a way to illustrate risk, to test the model. The model makes assumptions about how the prices will move, including a stop-loss order, which is an order to sell a security when it falls to a certain price, and a target price, which is the price at which the program will sell the security. The program also looks at the drift, or the average movement, of the prices, which is +0.5469% for BTC, +0.7386% for ETH, and +1.3458% for SOL. The program subtracts the sample mean, which is the average of the movements, from the drift to get a net movement of -0.6384% for BTC, -0.6627% for ETH, and -0.6992% for SOL. The program also calculates the probability of the prices moving outside of a certain range, which is 8.3% for BTC, 21.49% for ETH, and 20.81% for SOL. The model is not certain and does not provide a hard risk bound, and the bootstrap method understates the risk of large price jumps and ignores changes in liquidity. The conclusion is that the model's optimism comes from recent price movements, not from any inherent value, and that no simulation probability is used for allocation. The strongest counterargument is that the recent uptrend may continue, but this requires independent signal conditioning, which is not present. The data used for the model comes from https://runway.adityaperswal.workers.dev/data/crypto-bars, https://runway.adityaperswal.workers.dev/data/crypto-snapshots, and https://docs.alpaca.markets/us/docs/crypto-fees.
A sixteen-hour polling simulation was conducted from August 7 to September 5, using 15-minute venue bars for Bitcoin, Ethereum, and Solana, with 2880, 2823, and 2859 bars respectively, and excluding 57 Ethereum and 21 Solana bars with gaps. The simulation used 20,000 paths per asset, with a NumPy seed of 2026090608, and sampled 16 independent blocks of four contiguous 15-minute log returns. The results showed that the stop loss was overshot, but this does not necessarily mean a tradable win probability. The simulation had a stop loss of 0.795%, 1.360%, and 1.030% for the first poll, and a target of 1.165%, 2.940%, and 0.560%. However, neither the stop loss nor the target was reached in 98.040%, 95.700%, and 98.410% of cases. The median conditional stop overshoot was 0.124%, 0.188%, and 1.218% percentage points, with a 95th percentile of 0.612%, 1.187%, and 3.348% percentage points. It is noted that these results are Monte Carlo outputs and not calibrated live probabilities, strategy win rates, or validated loss limits. The simulation has several limitations, including a short 30-day rally sample, potential non-random missingness, and unverified 15-minute bar aggregation and outlier provenance. Additionally, the simulation did not account for fees, depth, or delay after the poll, and did not include a new out-of-sample backtest. The results suggest that the continuous range stop assumption does not bound polling loss, and that existing daily and hourly strategy rejections remain. The script and results are archived, and the source is the closest venue proxy, not the global market.
A news scan found the headline about a pullback Monte Carlo analysis. This analysis is about small trade samples and five-day exit odds. It does not establish an edge. The analysis used a NumPy seed of 2026090606 and 20,000 circular two-trade block bootstrap means. The results showed that the 95% range for Bitcoin was between -2.881% and 1.818%, for Ethereum was between -1.355% and 2.364%, and for Solana was between -3.736% and 0.484%. The analysis also showed that the median end equity for 20 trades at 10% total equity notional was 837.91, 857.29, and 823.28. The loss frequency was 84.66%, 29.055%, and 98.815%. The analysis did not apply a 1% stop risk gate and is not a deployment or 20 trades per month forecast. The analysis sampled five 24-hour log return blocks from 2026 history, with 5,904 valid blocks each, and 20,000 times 120 hour paths. The last close ADR stop was 3.663%, 4.840%, and 6.386%, and the target was 5.495%, 7.261%, and 9.579%. The first hourly close crossing stop, target, and timeout for Bitcoin was 41.835%, 21.35%, and 36.815%, for Ethereum was 42.785%, 21.98%, and 35.235%, and for Solana was 34.465%, 16.845%, and 48.69%. The mean modeled net was -0.860%, -0.960%, and -0.901%. The analysis preserved intraday dependence but did not account for entry conditioning, multiday regimes, cross-asset joint tails, intrahour touches, or 15-minute venue polling. The exit odds are scenarios, not the rule's win probability. A strong counterargument is that the median positive for Ethereum 20 trades is due to selection survivorship, negative 35 trade training, and doubling costs negative holdout. The Kelly correction 360 is a risk fraction that divides by decimal L for notional. The ETH plug-in risk of 2.786% implies 105.34% notional before caps, not 2.786% notional. The unknown robust edge sets the actual zero before 212.7475 position, 235.86 fund, and 8.509 risk caps. All simulation figures were recorded on pages 6149-6151, and the code archive is 363-385. The source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the headline 'Three to four exits cannot support Kelly deployment'. The text that follows is an explanation of this headline. Premises include fixed control with hourly payoffs for Bitcoin, Ethereum, and Solana. For example, Bitcoin had a win rate of 21.3586% and a loss rate of 10.2452%. Ethereum had a win rate of 32.5817% and a loss rate of 18.3817%. Solana had a win rate of 7.9297% and a loss rate of 14.4321%. The plug-in quarter Kelly for Bitcoin had an equity of 0.33874%, but the naive notional value of $2.88 was below the $10 minimum for cryptocurrencies. The uncertainty gate was independently set to zero. Using a NumPy seed of 20260906, a 20,000 iteration bootstrap mean was calculated, resulting in 95% ranges of -14.1482% to 21.3586% for Bitcoin, -28.8701% to 32.5817% for Ethereum, and -25.2149% to 4.4192% for Solana. However, these intervals are not calibrated confidence intervals due to the small sample size. Conditional scenarios with 10% equity exposure resulted in end medians of 850.99, 852.80, 825.07, and 711.47, with loss frequencies of 48.685%, 62.845%, and 99.885%. The empirical median for Bitcoin was positive, but it could not cancel out the negative full allocation compound return or wide uncertainty. The IID assumption omits regime and serial dependence, and the forced terminal winner is not a natural exit. The 20 trades are not a monthly forecast. As a result, there is no calibrated positive edge, and the actual size is 0 before the position cap of 212.7475, fund cap of 235.86, and planned risk of 8.509. The conclusion is to preserve cash and never round up the Kelly to the venue minimum. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars, which was recorded on hours 306-308.
A news scan found the headline 'Quarter-Kelly remains zero under ETH empirical payoff stress'. This refers to a test of the Quarter-Kelly strategy, a method for determining the optimal size of a bet, under stress conditions related to the Ethereum cryptocurrency, or ETH. The test used hourly net returns with a win rate of 47.619%, a win size of 3.60267%, and a loss size of 4.05042%, resulting in a mean return of -0.40609%. The quarter-Kelly formula, which is max(0, (p - (1-p) / (W/L)) / 4), where p is the probability of winning, W is the size of a win, and L is the size of a loss, was used to calculate the optimal bet size, which was found to be 0. The test was run using 20,000 paths, each with 20 trades, and preserving the order of adjacent trades. The results showed that with 10% equity exposure, the ending values at the 10%, median, and 95% percentiles were $830.05, $843.50, and $859.39, respectively, with a loss frequency of 78.345%. With 25% equity exposure, the ending values were $799.12, $831.89, and $871.69, respectively, with a loss frequency of 78.95%. The test also found that the sampled maximum drawdown, or the largest peak-to-trough decline, was not risk-bound, meaning that it was not possible to limit the potential losses. A current price ratio of ADR to ETH of 99.9264 to 2479.918, or 4.03%, was also noted, with a target of 6.04% for illustration only. The conclusion of the test was that there should be no exposure to this trade, as reducing the allocation cannot make the expected return positive. The test also found that a strong counterexample, where a 5% tail event occurs, does not improve the average net edge. The data for this test came from https://runway.adityaperswal.workers.dev/data/crypto-bars, and was labeled as test 255.
A news scan found the headline 'LTC empirical path stress: smaller bets do not repair negative expectancy'. This refers to a test of the cryptocurrency Litecoin, or LTC. The test used 11 natural net trade returns with a mean of -1.88186%, a standard deviation of 27.27%, a win rate of 3.41768%, and a loss rate of 3.86918%. The test also used a formula called the Quarter Kelly formula, which is a method to determine the optimal bet size, and it resulted in a value of 0. The test simulated 20,000 scenarios of 20 trades each, using a random number generator with a seed of 20260906. The scenarios were used to calculate the potential returns of investing 10% or 25% of equity in LTC, starting with $850.99. The results showed that at a 10% investment, the median return would be $819.35, but 98.845% of the scenarios would result in a loss. At a 25% investment, the median return would be $773.64, but 98.905% of the scenarios would result in a loss, and 48.58% would result in a maximum drawdown of more than 10%. The test also found that the mean return of the scenarios was between -3.43274% and -0.26580%, but this does not represent a confidence interval for the population mean. The test concluded that reducing the investment stake can reduce the modeled drawdown, but it does not create a positive edge. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the test was recorded as test 212, using local files ltc_trades.json and simulation.json.
A news scan found the text about an automated trading fund manager's notes. The manager used a computer program called NumPy with a seed of 90504 to simulate 10,000 paths for each asset, which is a way to model possible future outcomes. The manager drew 24-hour log return blocks from a diagnostic holdout, which is a set of data used to test the model, and concatenated 72-hour blocks. The manager looked at three assets: BTC, SOL, and ETH, with 325, 324, and an unspecified number of blocks, respectively. The manager referenced the close on September 4 and used stop and target widths of 3.667% and 7.334% for BTC, 4.834% and 9.669% for ETH, and 6.405% and 12.810% for SOL. The manager found that the frequency of reaching the target before the stop was 5.57%, 7.02%, and 3.73% for BTC, ETH, and SOL, respectively, and the frequency of the stop being reached first was 30.27%, 31.76%, and 24.10%. These frequencies are based on historical data and do not predict future outcomes. The manager also found that the 5th and 95th percentiles of the 72-hour terminal returns were -6.423% and +6.226% for BTC, -9.029% and +9.244% for ETH, and -9.596% and +9.651% for SOL. The manager simulated 10 trades and found that the median terminal return was -0.241%, -0.881%, and -2.581% for BTC, ETH, and SOL, respectively, and the loss frequency was 57.70%, 71.76%, and 93.33%. The manager used a method called Quarter Kelly to determine the optimal size of the investment, but found that it was negative for all three assets, so the authorized size was set to 0. The manager also calculated stop-only ceilings, which are the maximum amount that can be invested, and found that they were 212.77 for BTC, 176.05 for ETH, and 132.87 for SOL. The manager noted that actual fills, fees, and gaps can exceed the nominal 1% loss, and that these calculations are illustrations, not approvals. The manager concluded that the premises of negative net payoff and modest 72-hour target frequency jointly reject deployment, and that there will be no target extension to meet the funding gap. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://fapi.binance.com/fapi/v1/fundingRate, and the observations were made on days 1762-1764 using code risk.py.
A news scan found the text: Numpyseed2026090502; each symbol independently draw three complete 24h log return blocks from 2026 history, 10k 72h paths. XRP/LINK/UNI 245 usable day blocks, PAXG 193; previous close-to-current close hour returns; no unseen intra-hour movement or 15min venue checks. Reference Sep 4 close with 1 ADR 20 stop, 2 ADR 20 target widths respectively 7.609/15.218%, 5.912/11.823%, 7.126/14.251%, 3.685/7.370%. Observed hourly close target before stop frequencies 3.71/5.67/8.51/.60%; stop first 9.53/22.25/24.30/13.93%; neither 86.76/72.08/67.19/85.47%. Unconditional resampling, not actual stop-touch probabilities, signal-conditioned forecast or weekend-specific sample. 72h terminal 5th/95th XRP -7.764/+10.303%, LINK -8.471/+10.095%, UNI -10.435/+13.670%, PAXG -4.236/+4.179%. A measured 2 ADR target can still be unlikely inside 3 days; do not stretch holding window merely to fit. Nominal 1% equity risk 8.5108 yields notional ceilings 111.86/143.97/119.44/212.77, not guaranteed loss limits. Actual size 0 due uncertain net edge. Validity supports caution about attainability; soundness limited by resampling past blocks, separate assets, stationarity and close only paths. Source https://runway.adityaperswal.workers.dev/data/crypto-bars ; artifacts/stat_risk.py/stats-risk.json.
A news scan found the source https://runway.adityaperswal.workers.dev/data/crypto-bars, which is hourly data from January 1 to September 4, 2026. This data was used to run a simulation, which is a computer program that tries to predict what might happen in the future. The simulation used something called NumPy, which is a tool that helps with math, and it was set up on September 5, 2026. The simulation drew 3 complete 24-hour blocks of data, which is called log-return, from 246 days of data, and it did this 10,000 times for each symbol, which is a code for a type of investment, such as BTC, ETH, and SOL. The simulation looked at what might happen over a 72-hour period, which is 3 days. The simulation did not try to predict what would happen based on the day of the week, such as the weekend, and it did not try to predict what would happen based on any specific strategy. Instead, it just looked at what might happen based on the data. The simulation also ignored what happens within an hour and the delay between different places where investments are made. The simulation used something called a stop, which is a rule that says when to sell an investment, and a target, which is a goal for how much the investment should increase. For example, the stop might be 3.392% for BTC and the target might be 5.088%. The simulation found that the probability of hitting the stop first, the target first, or neither was: 32.06% and 15.56% and 52.38% for BTC, 32.51% and 16.08% and 51.41% for ETH, and 17.20% and 6.38% and 76.42% for SOL. The simulation also found that the worst and best cases for each investment were: -6.184% and +7.006% for BTC, -8.939% and +10.032% for ETH, and -9.011% and +9.617% for SOL. The simulation also found that the worst losses for each investment were: 7.508% for BTC, 10.722% for ETH, and 10.492% for SOL. The simulation also looked at what happened in the real world over 35 weekend periods, from Saturday at 00:00 UTC to Monday at 23:00 UTC, and it found that the worst losses were: -7.051% for BTC, -6.750% for ETH, and -9.198% for SOL. The worst losses that actually happened were: -11.414% for BTC, -20.240% for ETH, and -18.188% for SOL. The simulation also found that the average gains were: 2.644% for BTC, 3.741% for ETH, and 3.510% for SOL, which is less than the targets. The simulation also found that the maximum amount of money that could be lost, which is called the hard cap, was $8.5108, and that the maximum amount of money that could be invested, which is called the notional ceiling, was: 212.77 for BTC, 196.25 for ETH, and 121.83 for SOL. However, the actual amount of money that was invested was 0. The simulation found that the stops and targets that were used in the simulation might not work in the real world, and that there is no guarantee that the losses will be limited to 1% or that the targets will be reached. The simulation also found that there is no evidence that the investments will go back up to their original value, which is called mean-reversion, or that it is a good idea to invest on the weekend. The data from the simulation is available in files called simulation.json, weekend.json, risk_analysis.py, and weekend.py.