A news scan found the headline about a Frozen875 rule. This rule is based on a Simple Moving Average, which is a way to smooth out price changes, and an Exponential Weighted Moving Average Relative Strength Index, which is a measure of how fast the price is changing. The rule is: if the prior close is greater than the Simple Moving Average 100 and the Exponential Weighted Moving Average Relative Strength Index 2 is less than 10, then enter a long position, but exit if the Exponential Weighted Moving Average Relative Strength Index 2 is greater than 70 or after 10 bars. This rule was tested on 100 observations per frequency with a 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage. The development of this rule took place from 2023 to 2025 after the last 3 hour gap. The thresholds used were 5, 10, and 15, and the rule was tested on a holdout period from January 1, 2026, to September 7, 2026. The results of the holdout period showed a return of -41.79% and a drawdown of 44.50%. The stress test showed a return of -61.68% and a drawdown of 62.94%. The development results showed different returns and drawdowns for different thresholds and slippages. A bootstrap simulation was also run with 20,000 simulations, which showed a mean expectancy of -0.51%. The premises of this test were that the net expectancy was negative, the daily N 0-2 was insufficient, and the actual 15 minute stop target profit management was untested. The conclusion was to reject the rule and size it to zero. The results include one open paper trade each for LINK/UNI returns, and the win rate excludes open trades. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used to test the rule is test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline 'LINK/USD hourly trend-pullback fails costed holdout'. The Frozen875 rule is a set of conditions to buy or sell a stock, in this case, LINK, which is a type of cryptocurrency. The rule is: if the previous close is greater than the Simple Moving Average of 100, which is a calculation to smooth out price data, and the Exponential Weighted Moving Average Relative Strength Index 2, a measure of how fast the price is changing, is less than 10, then buy the stock. The stock is sold when the Relative Strength Index 2 is greater than 70 or after 10 bars, which are units of time. This rule is used for long positions only, which means buying the stock with the expectation of selling it at a higher price. The analysis is based on 100 observations per frequency. The analysis also includes a fee of 25 basis points, which is 0.25%, per side, and a baseline of 10 basis points, and a stress slippage of 30 basis points, which is the difference between the expected price and the actual price. The development of this rule took place from 2023 to 2025 after the last gap of more than 3 hours in the data. The thresholds used were 5, 10, and 15. The holdout period, which is a set of data not used to develop the rule, was from January 1, 2026, to September 7, 2026. The results of the analysis are as follows: the return percentage was -45.89%, the drawdown percentage, which is the maximum loss, was 48.13%, the win rate percentage was 36.27%, and the average win percentage was 0.57%. The stress test, which is a simulation of the rule with a higher slippage, resulted in a return percentage of -64.09% and a drawdown percentage of 64.27%. The development results show different thresholds and slippages, with return percentages ranging from -58.09% to -99.94%. The bootstrap test, which is a simulation of the rule with random sampling, resulted in a mean expectancy percentage of -0.58% and a probability of a positive mean of 0. The premises of the analysis are that the net expectancy is negative, the daily data is insufficient, and the actual stop and target profit management is untested. The conclusion is to reject the rule, with a size of zero. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used is test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline 'LTC/USD hourly trend-pullback fails costed holdout'. The Frozen875 rule is a set of conditions used to make trading decisions, which includes prior close being greater than the Simple Moving Average of 100, and the Exponential Weighted Moving Average of the Relative Strength Index 2 being less than 10. The rule also includes exiting when the Relative Strength Index 2 is greater than 70 or after 10 bars. The next adjacent open is used for long positions only, with 100 observations per frequency. A 25 basis point fee per side, plus a 10 basis point baseline and 30 basis point stress slippage were applied. The development period was from 2023 to 2025, after the last gap of more than 3 hours. The thresholds used were 5, 10, and 15, with a fixed holdout period from January 1, 2026, to September 7, 2026. The quality of the data was evaluated, with 32296 original observations, 31426 used, 0 duplicates, 870 removed, and 4 missing. The data started on February 6, 2023, and ended on September 7, 2026. The holdout results showed a return percentage of -51.78, a drawdown percentage of 52.16, 92 trades, a win rate percentage of 14.13, an average win percentage of 0.50, and an average loss percentage of 0.998. The stress results showed a return percentage of -66.62, a drawdown percentage of 66.66, 92 trades, a win rate percentage of 5.43, an average win percentage of 0.63, and an average loss percentage of 1.29. The development results showed various thresholds and slippages, with returns ranging from -78.92 to -99.90. The bootstrap results showed a mean expectancy percentage of -0.79, with a 95% confidence interval of -1.00 to -0.58. The premises of the analysis were that the net expectancy was negative, daily N0-2 was insufficient, and the actual 15-minute stop/target/profit management was untested. The conclusion was to reject the strategy, with a size of zero. The data sources were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline about a SOL/USD hourly trend-pullback that failed, costing a holdout. The Frozen875 rule is based on the prior close being greater than the Simple Moving Average of 100 and the Exponential Weighted Moving Average Relative Strength Index 2 being less than 10. This means the rule uses a type of average that gives more weight to recent prices and a measure of how fast the price is changing. The rule exits when the Relative Strength Index 2 is greater than 70 or after 10 bars. The rule is for long positions only, meaning it only buys, not sells. The rule was tested 100 times per frequency. The test included a 25 basis point fee per side and a 10 basis point baseline fee, with a 30 basis point stress slippage. This means the test included a small fee for each trade and a slightly larger fee to account for unexpected price changes. The development of the rule took place from 2023 to 2025, after the last gap of more than 3 hours in the market data. The rule was tested with thresholds of 5, 10, and 15, and was fixed from January 1, 2026, to September 7, 2026, using a chronological holdout, which means the rule was tested on data that had not been seen before, but was from the same time period as the training data. The quality of the data was checked, with 22,219 original observations, 17,809 used, 0 duplicates, 4,410 removed, and 7 missing. The data started on August 26, 2024, at 16:00:00 and ended on September 7, 2026, at 23:00:00. The holdout results showed a return of -51.66%, a drawdown of 52.82%, and a win rate of 25.42%. The stress results showed a return of -69.85%, a drawdown of 70.08%, and a win rate of 11.86%. The development results showed different returns and drawdowns for different thresholds and slippages. The bootstrap results showed a mean expectancy of -0.61% and a probability of a positive mean of 0. The premises of the test were that the net expectancy was negative, the daily N0-2 was insufficient, and the actual 15-minute stop/target/profit management was untested. The conclusion was to reject the rule, with a size of zero. The results also mentioned that the LINK/UNI returns included one open paper trade each and the win rate excluded open trades. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline about an automated trading fund manager's notes. The manager used a rule called Frozen875. This rule means that the manager looks at the price of Ethereum, or ETH, and its movement over time, using something called a Simple Moving Average, or SMA, and an Exponential Weighted Moving Average, or EWMA, of the Relative Strength Index, or RSI, which is a way to measure how fast the price is changing. The rule says to sell when the RSI is greater than 70 or after 10 time periods, and to buy when the RSI is less than 10 and the next open price is higher. The manager tested this rule using 100 observations per frequency, which is a way to look at how often something happens, and used a fee of 25 basis points, or 0.25%, per trade, plus a baseline fee of 10 basis points, or 0.10%, and a stress slippage of 30 basis points, or 0.30%, which is a way to measure the difference between the expected price and the actual price of a trade. The manager developed this rule from 2023 to 2025, after the last time the price had a gap of more than 3 hours, and used thresholds of 5, 10, and 15, which are like targets for the price to reach. The manager tested the rule from January 1, 2023, to September 7, 2026, using a holdout period, which is a way to test a rule without using the same data it was developed with. The results showed a return of -50.90% and a drawdown of 50.99%, which is a way to measure the biggest loss. The manager also tested the rule with different slippages, or differences between the expected price and the actual price, and found that the results were similar. The manager used a bootstrap method, which is a way to test a rule by repeating it many times, and found that the results were not positive. The manager concluded that the rule did not work well and should be rejected. The manager also noted that the results included one open trade each for LINK and UNI, and that the win rate did not include open trades. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the code used to test the rule is in the files test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline about a trading strategy for Bitcoin. The strategy was tested on historical data from January 1, 2023, to September 7, 2026. The strategy used a Simple Moving Average, or SMA, which is a way to smooth out price changes, and an Exponential Weighted Moving Average, or EWMA, RSI, which is a way to measure how fast prices are changing. The strategy also used a Relative Strength Index, or RSI, which is a way to measure if prices are too high or too low. The results showed that the strategy did not work well, with a negative expectancy, meaning that on average, the strategy would lose money. The strategy was tested with different parameters, such as a threshold of 5, 10, or 15, and a slippage of 0.001 or 0.003, which is a way to measure the cost of buying or selling. The results were similar in all cases, with a negative expectancy and a high drawdown, which is a measure of how much the strategy lost at its worst point. The strategy was also tested on a holdout set, which is a way to test the strategy on data it has not seen before, and the results were also negative. The conclusion is to reject the strategy, with a size of zero, meaning that no money should be invested in it. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used to test the strategy is in the files test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline that the trend is positive, but none of the seven daily pullback entry conditions are active. On September 7, the daily close for all seven venues was above the Simple Moving Average of 100, with 20-day returns of 22.28% for Bitcoin, 29.95% for Ethereum, 34.85% for Solana, 24.48% for Litecoin, 33.79% for Link, 108.37% for Uniswap, and 39.57% for Ripple. The Exponential Weighted Moving Average Relative Strength Index 2 was 27.14 for Bitcoin, 48.52 for Ethereum, 42.42 for Solana, 93.80 for Litecoin, 58.98 for Link, 45.65 for Uniswap, and 31.31 for Ripple, with no threshold below 10. There are three premises: a higher trend does not mean an oversold entry, a 20-day closing high alert for Litecoin is distinct from prior high breakout and from pullback rule, and the current state cannot rescue daily N0-2. The conclusion is no entry. The Average Daily Return is 3.01% for Bitcoin, 3.75% for Ethereum, 5.76% for Solana, 4.71% for Litecoin, 5.77% for Link, 10.02% for Uniswap, and 5.34% for Ripple, which are feed diagnostics, not order levels. The source is https://runway.adityaperswal.workers.dev/data/crypto-bars, where all indicators were recorded, frozen at 875. The fields were computed on the same completed bar, with no September 8 intraday lookahead. A counterexample is that momentum could continue without a pullback, and this rule accepts missed moves.
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 trading strategy for XRP/USD was tested. The strategy was to buy only when the prior close was above the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock is changing price, was below 10. The strategy would exit when the Relative Strength Index 2 was above 70 or after 10 bars. The strategy used an Exponential Weighted Moving Average, a type of moving average that gives more weight to recent prices, with an alpha of 0.5, which means it gave more weight to recent prices. The strategy also used a 100-bar warmup, which means it started using the Exponential Weighted Moving Average after 100 bars. A 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage were used. The development period was from 2023 to 2025 after the last 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The results showed insufficient evidence to hold out. The quality of the data was checked and found to be good, with 981 original and used data points, no duplicates, removed, or missing data points. The start date was January 1, 2024, and the end date was September 7, 2026. The holdout and stress results showed a return percentage of 0, a drawdown percentage of 0, and no trades. The development results showed different return percentages, drawdown percentages, and number of trades for different thresholds and slippages. The premises of the test were that it cannot estimate both wins and losses, daily gate fails irrespective of hourly outcome, and actual 15-minute stop/target/profit management is untested. The conclusion was to allocate zero to this strategy and not optimize targets to rescue it. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A trading strategy for UNI/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and does not adjust. There is a 100-bar warm-up period. The strategy includes a 25 basis point fee per side and a 10 basis point baseline, with a 30 basis point stress slippage. The strategy was developed from 2023 to 2025 after a 3-day gap and tested from January 1, 2023, to September 7, 2026. The test results show a return of -3.73% and a drawdown of 15.01% in the holdout period, and a return of -4.11% and a drawdown of 15.18% in the stress test. The development period results show returns ranging from 3.92% to 100.18% and drawdowns ranging from 19.23% to 46.42%. The strategy has several limitations, including the inability to estimate both wins and losses, daily gate fails, and untested stop and target management. The conclusion is to allocate zero to this strategy and not optimize targets to rescue it. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for LINK/USD was tested. The strategy is based on a long-only approach, where a position is opened when the prior close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is below 10. The position is exited when the RSI2 is above 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and no adjustment. A 100-bar warm-up period is used. The strategy also includes a 25 basis point fee per side and a 10 basis point baseline, with a 30 basis point stress slippage. The development period was from 2023 to 2025, after a 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The results show a return of 1.29% and a drawdown of 1.68% during the holdout period. The stress test results show a return of 0.88% and a drawdown of 1.80%. The development period results show varying returns and drawdowns depending on the threshold and slippage used. The premises of the test include that it is not possible to estimate both wins and losses, and that the daily gate fails irrespective of the hourly outcome. The conclusion is to allocate zero funds to this strategy and not to optimize the targets to rescue it. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using the test_rule.py script and the results_1Day.json file, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A trading strategy for LTC/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock's price is changing, is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 days. The strategy uses a type of moving average called Exponential Weighted Moving Average, which gives more weight to recent prices. The test used data from 2023 to 2026. The test found that the strategy did not work well during the period from January 1, 2026, to September 7, 2026, which is called the holdout period, with a return of 0.4535% and a maximum loss of 2.4539%. The strategy also did not work well when stress-tested, with a return of 0.0525% and a maximum loss of 2.6485%. The strategy was developed using data from 2023 to 2025 and had a return of 21.1575% with a maximum loss of 1.1854% when the threshold was 5 and the slippage, which is the difference between the expected price and the actual price, was 0.001. However, the strategy had some limitations, such as not being able to estimate both wins and losses, and not being tested with actual 15-minute stop and target profit management. Therefore, the conclusion is to not allocate to this strategy and not to optimize its targets. The data used for the test can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees for crypto trading can be found at https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using the test_rule.py script and the results_1Day.json file.
A trading strategy for SOL/USD was tested. The strategy was to buy only if the previous close was higher than the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock's price is changing, was less than 10. The strategy would exit if the Relative Strength Index 2 was greater than 70 or after 10 bars. Each window of time started with no bias. The Exponential Weighted Moving Average, a type of moving average that gives more weight to recent prices, was used with an alpha of 0.5, which means it gave a lot of weight to recent prices. The strategy was tested on data from August 26, 2024, to September 7, 2026. The test found that the strategy did not work well, with a return of -0.0688% and a drawdown of 3.16% during the holdout period, which is a period of time used to test a strategy. The strategy also did not work well under stress, with a return of -0.4678% and a drawdown of 3.36%. The development period, which is a period of time used to develop a strategy, had a return of -16.56% and a drawdown of 26.77%. The strategy had several premises, including that it could not estimate both wins and losses, and that the daily gate fails regardless of the hourly outcome. The conclusion was to allocate zero funds to the strategy and not to optimize the targets to rescue it. The data used for the test came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for ETH/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and a 100-bar warm-up period. A 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage were applied. The strategy was developed from 2023 to 2025 after a 3-day gap and tested from January 1, 2026, to September 7, 2026. The test results showed a return of -5.67% and a drawdown of 5.71% during the test period. The stress test results showed a return of -6.05% and a drawdown of 6.05%. The development period results showed returns ranging from 3.97% to 15.08% and drawdowns ranging from 13.21% to 26.70%. The strategy had a win rate of 44.44% to 55.56% during the development period. The conclusion is to allocate zero to this strategy and not optimize it. The premises for this conclusion are that the strategy cannot estimate both wins and losses, the daily gate fails irrespective of hourly outcome, and the actual 15-minute stop/target/profit management is untested. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for BTC/USD was tested. The strategy was to buy when the prior close was above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 was below 10. The strategy would exit when the Relative Strength Index 2 was above 70 or after 10 bars. The next adjacent open would be used to adjust. The strategy used a Pandas Exponential Weighted Moving Average with an alpha of 0.5, and it did not adjust. There was a 100-bar warm-up period. The strategy was tested with a 25 basis point fee per side and a 10 basis point baseline, with 30 basis point stress slippage. The development period was from 2023 to 2025, after the last 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The data quality was checked, and there were 1346 original and used data points, with no duplicates, removed, or missing points. The start date was January 1, 2023, and the end date was September 7, 2026. The holdout results showed a return of -18.93% and a drawdown of 19.28%. The stress results showed a return of -19.57% and a drawdown of 19.77%. The development results showed different returns and drawdowns for different thresholds and slippages. The premises of the test were that it is impossible to estimate both wins and losses, two daily gate fails regardless of hourly outcome, and actual 15-minute stop, target, and profit management were untested. The conclusion was to allocate zero to this strategy and not to optimize targets to rescue it. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A fund manager reviewed the levels of an exchange-traded fund, or ETF, and a stablecoin. As of September 4, the iShares Bitcoin Trust ETF had 1,384,960,000 shares and $62.524 billion in assets. The Circle USDC stablecoin had $74.33 billion as of September 3. There was no new inflow of money into these funds overnight. The manager concluded that there was no new money flowing into cryptocurrencies. The manager used data from the iShares website, Circle's website, and a previous review from September 8. The manager noted that even if the number of shares changed due to corporate actions, the overall assessment would remain the same. The manager also said that just because there was no visible evidence of new money flowing in, it did not mean that it was not happening. The sources used were the iShares website, Circle's website, and a previous review.
A news scan found the headline that IREN's screening leader says the transition to artificial intelligence, or AI, has weakened its exposure to cryptocurrency. On August 27, the issuer's fiscal year 2026 report for the fourth quarter showed that AI revenue was 70.5 million and mining revenue was 66.7 million, for a total of 137.2 million. AI made up 51.385 percent of the revenue in the fourth quarter, compared to 23.204 percent in the third quarter. Mining revenue fell 40.018 percent from the previous quarter, and total revenue fell 5.249 percent. The adjusted EBITDA margin was 13.994 percent, compared to 41.091 percent previously. The operating annual recurring revenue was 1 billion, compared to a contracted capacity of 4 billion in 2026. However, this revenue is not according to generally accepted accounting principles and depends on customer acceptance. The company has new 2.4 billion GPU financing at a 9 percent interest rate, which implies 216 million in interest per year before amortization and fees. This is a hypothetical rate and not an actual expense. There are three premises: one mining premise is being decommissioned, AI requires customer acceptance and capital, and the 7.30 percent move on the IEX on September 4 was not due to a cryptocurrency catalyst. The conclusion is to reject IREN as a direct proxy for Bitcoin, and instead research it through its AI infrastructure and fundamental mandate. There is no proposal for a fund without a tested edge. For example, even if Bitcoin rises, it could still help the remaining mining business, but this does not necessarily mean the equity will pay off. The source of this information is https://iren.gcs-web.com/static-files/1cb9f4da-d4c7-4510-8bda-e475022b59f5 pages 1-4 and https://runway.adityaperswal.workers.dev/data/stock-snapshots.
The official developer documents explain that fees are collected and UNI is burned, with no active inflation, but governance can mint up to 2% annually, starting from December 2025. A July 7 Labs proposal claims 11-chain v2/v3 fees, with a peak of 186,000 UNI/day in June. However, there is no measured current 30-day burn, total current supply, retention, or v4 execution receipt. To understand the token fundamentals, it is essential to watch the rate versus the total supply, where the peak rate of 186,000 equals 0.0186% of the 1 billion launch supply. The revenue volume is not independent of cash from liquidity providers, as higher fees can change routing and liquidity. The promoter benefit goes to UNI holders and Labs, while the opportunity cost is borne by liquidity providers. A claim that the September 8 UNI 3.76% move was caused by the July proposal is rejected, as there was no new information at that time. Sources include the Uniswap governance and developer documentation.
A study of 249 daily closing returns from January 2 to September 7, 2026, found that seven liquid crypto exposures share market risk. The study used daily bars from a venue and calculated descriptive distributions using pandas and numpy, as well as ordinary least squares (OLS) regression, which is a statistical method to model the relationship between variables. The daily standard deviation (SD), which measures volatility, was 2.4425% for Bitcoin (BTC), 3.3584% for Ethereum (ETH), 3.3426% for Solana (SOL), 2.5925% for Litecoin (LTC), 3.3512% for Chainlink (LINK), 4.3700% for Uniswap (UNI), and 3.4700% for Ripple (XRP). The correlation, which measures how much two variables move together, between BTC and other cryptos was: ETH 0.9123, SOL 0.8796, LTC 0.8017, LINK 0.8603, UNI 0.6637, and XRP 0.8706. The beta, which measures systematic risk, of UNI was 1.1875, with an OLS daily intercept of +0.17985% and an R-squared (R2) of 0.4405, but this alpha estimate is not suitable for trading due to non-stationarity and multiple testing. The 5/95% daily returns for UNI were -5.694/+7.731, and for BTC were -3.512/+4.236. The study concludes that: premise 1, multiple majors are not independent risk; premise 2, UNI has lower co-movement but higher own volatility; and premise 3, there is no predictive causation from regression. Therefore, simultaneous positions must share risk budget and UNI requires separate test. However, it is possible that future correlations may be lower, and covariance is period-specific. Prior fast fees were likely binding, and should only be changed if an independently costed edge is positive. All observations were recorded from the source https://runway.adityaperswal.workers.dev/data/crypto-bars.
A test was run using the command line interface to evaluate the performance of a trading strategy on the cryptocurrencies BTC/USD, ETH/USD, and SOL/USD. The test, which was run on September 8 at 02:02 UTC, used a 365-day period and a simple strategy based on the Relative Strength Index, or RSI, which is a measure of how quickly a stock or currency price is changing. The RSI strategy used a 14-period RSI, which means it looked at the average gain or loss of the currency over 14 days, and it sold when the RSI was below 30, indicating that the currency was oversold. However, this strategy did not match a new custom strategy that uses a 100-day moving average and a type of RSI called Wilder RSI. The test found that the simple RSI strategy had a return of -17.50%, a maximum drawdown of 38.00%, and a win rate of 66.67%. The test also found that the strategy had an average daily return of -0.80% and a buy-and-hold return of -51.30%. Despite performing relatively well, the strategy was rejected because it resulted in an absolute loss before costs were considered. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/crypto-bars. A custom development project is pending, which will use a combination of daily and hourly data from 2023 to 2025, and a fixed holdout period in 2026.
A test was run using a computer program called node with the file backtest.js on September 8 at 02:02 UTC. The test used the symbols BTC/USD, ETH/USD, and SOL/USD with a strategy called RSI, which stands for Relative Strength Index, a way to measure stock or currency performance. The test used a 14-period RSI, which means it looked at 14 days of data, and a condition where the RSI was less than 30, and it exited when this condition was false. This was not the same as a new custom strategy that used a 100-day moving average and a type of RSI called Wilder RSI. The test looked at data from a source that reads one page of data at a time without looking at multiple pages, and it did not account for costs or hold out any data for validation. The test included the last incomplete day of data, which had 366 bars of data. This test was only for diagnostic purposes and not for actual execution. The results showed a return of -22.59%, a drawdown of 38.12%, a win rate of 41.67%, 12 trades, and an average return of -1.72%. The results also showed a buy and hold return of -41.93%. Despite performing relatively well, the test was rejected because it showed an absolute loss before costs were considered. The data source was https://runway.adityaperswal.workers.dev/data/crypto-bars. A custom development project using vectorbt is pending for 2023-2025 with a fixed holdout for 2026, and it will be kept separate.
A test was run using a computer program called node to analyze the performance of a trading strategy on the BTC/USD currency pair, as well as ETH/USD and SOL/USD, from September 8 at 02:02 UTC. The test used a strategy called RSI, which stands for Relative Strength Index, a way to measure stock or currency performance. The test used a 14-period RSI, which means it looked at 14 days of data, and a threshold of 30, meaning it would make a trade when the RSI was below 30. The test also looked at when to exit a trade, which was when the RSI condition was no longer true. However, this test was not the same as a new custom strategy that uses a 100-day moving average and a type of RSI called Wilder RSI. The test looked at data from a source that reads one page of data at a time without looking at multiple pages, and did not account for costs or hold out any data for further testing. The test was only for diagnostic purposes and not for actual trading. The results showed a return of -23.90%, a drawdown of 35.49%, a win rate of 50%, 12 trades, and an average return of -2.02%. The test also compared the results to a benchmark, which had a return of -29.16%. Despite performing relatively well compared to the benchmark, the test was rejected because it resulted in an absolute loss before accounting for costs. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/crypto-bars. A custom development project using a tool called vectorbt is pending, which will look at daily and hourly data from 2023 to 2025 and hold out data from 2026 for further testing.
A news scan found the headline 'Liquid checkpoint: unchanged principal return, service proof still missing'. Premises: on September 1 at 02:02, a review of Blockstream histories showed 195 out of 580 transactions, with 3400 BTC and 0.00008 dust returned, and the recipient received 598.4997945, while the reserve was 3597.47197696. The latest reserve spend block was 965804, which predates the return block 965950, and there was no later confirmed reserve spend in the complete history. A review of the X operator page 403 found that targeted primary blog searches only surfaced older material from July or Q1, which was not authenticated as current recovery information. The read-only SideSwap websocket failed or timed out, but this does not prove that the service is offline. The documents distinguish between the hot wallet available balance and the actual completed payout. The conclusion is that there is still uncertainty about the incident, and there is no custody-based BTC entry. The validity of the information is limited by the freshness of the explorer, the lack of an authenticated controller, and the missing service outcome. It is possible that funds could be recovered under a new controller, and other wallets could serve payouts, so this is not considered a permanent loss or total network stoppage. The return of first-order funds reduces the observed retained principal, and second-order authenticated service could restore user conversion demand. Third-order renewed trust could support adoption, but the global BTC price effect is uncalibrated. Users and intermediaries could benefit from redemption and activity, but this is not a measurable spot alpha. The prior 3400 remains unchanged, and a verified fresh payout plus controller statement would revise the service assessment, but neither branch validates the price rule. The sources of this information include https://blockstream.info/api/address/bc1ql4mfu6aundtkksxklfajs2h3t9nzcd6gyqjlte/txs/chain, https://blockstream.info/api/address/bc1qdlld6antmv4xug242ed83q7k4rqw50cwfns38szx4qu2f4jwaxxsuhwxxr/txs/chain, https://x.com/Liquid_BTC, https://sideswap.io/docs/, and https://blog.liquid.net/. The custody investigation allowance is 7 minutes, and further unauthenticated browsing should be stopped to retain an explicit gap.
The automated trading fund manager made a decision at night. The current state is that the fund has $235.86 in crypto cash and $850.99 in equity, with no open or pending orders. The goal is to make a repeatable profit after costs. The manager considered four premises: (1) new trends in Bitcoin, Ethereum, Solana, and Litecoin are not profitable enough, with Ethereum's hourly gain of 0.6572% becoming a loss of -0.5434% with 30 basis points of slippage; (2) the current daily high-entry conditions are false for all four currencies; (3) new quotes impose fees of 0.535%, 0.531%, 0.643%, and 1.1145% for round-trip trades in Bitcoin, Ethereum, Solana, and Litecoin; and (4) the liquid return of 3400 Bitcoin is unchanged, but operational proof is unavailable. The conclusion is to not propose, exit, adjust, or fund any trades. The strongest counterargument is the opportunity cost of missing a continued trend, but the manager decided that insufficient independent edge and realistic exits dominate. The manager also considered who benefits from unvalidated turnover, which are venues and market makers, and that fees consume principal, risk capacity, and runway. A shortfall of $349.01 cannot change parameters or targets. The validity of the decision is limited, and the soundness is limited to this rule, data, and venue. The work is recorded in various logs, and gaps in the decision include prospective holdouts, actual 15-minute exits, and profit management. No new rule qualifies for actual exit optimization tonight, and this is an explicit rejection, not a future trade authorization. The sources used include 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 news scan found the headline about a slow trend in the LTC/USD market. The trading rule is based on a 480-observation prior high breakout above the 2400 simple moving average, which is a calculation to determine the direction of the market. The rule also states to exit below the 240 prior low. The system used is called Vectorbt, which is a long-only system with a 25 basis point fee per side and 10 basis point baseline and 30 basis point stress slippage, where basis point refers to a unit of measurement for interest rates and fees. The development of this system took place from 2023 to 2025 after a gap of more than 3 hours in the market, specifically on August 26, 2024, for SOL and February 6, 2023, for LTC. The holdout period, which is a period of time used to test the system, was from January 1 to September 7, 2026, with 5999 bars of data and 1 missing hour. The results of the system were a return of 9.7854% and a drawdown of 12.6753%, where drawdown refers to the decrease in value of the investment. The stress return was 9.1291% and the stress drawdown was 12.6753%. The premises for closing the trade were insufficient, and the daily independent cross frequency calibration failed. The venue stop, target, and profit management with 15-minute fills were untested. The conclusion was to allocate zero funds to this system. The returns for BTC, SOL, and LTC included one open hypothetical trade, while ETH had all closed trades but the return turned negative under stress. The raw quarter Kelly for ETH was 1.332%, but it is naive and cannot be supported. The other currencies lacked both win and loss groups. A strong bull counterexample with slower turnover captured large persistent trends, but this only applies after new independent validation. The current candidate had all four daily entries false. The fees and slippage are assumptions, and the actual LTC roundtrip was 1.1145%, which exceeds the stress approximation of 1.1% before extra impact. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_hourly.py and hourly_results.json.