A news scan found the report of an automated trading fund manager. The report analyzed data from October 14, 2025, to September 1, 2026. It looked at the next three days of open-to-open overlapping observations, which is a way to compare data, for 323 assets per type of asset. The report found that extreme negative funding, which is the money paid to hold a position, from the previous day resulted in a gross mean return of -0.6221% for Bitcoin, -0.0827% for Ethereum, and -3.5426% for Solana, compared to other days which were -0.2193%, -0.3447%, and +0.0184%. The report also used a statistical method called circular block bootstrap to analyze the data, which gives a range of possible outcomes. The results showed that the returns overlapped, meaning they were not independent, and the event counts were not independent trades. The report used a statistical model called OLS to analyze the relationship between funding and returns, and found that for Solana, more negative funding was associated with weaker subsequent returns, but this does not mean that one causes the other. The report also looked at the moving average, which is a way to smooth out data, and found that Solana showed a downtrend, or a decrease in value, of -2.12% in 10 out of 50 periods. The report concluded that the evidence favors continued weakness in Solana, but this is not a universal prediction. The report also stated that the current universe, multiple comparisons, and selected regimes limit the ability to make conclusions. The report rejected a fixed rebound rule, which is a rule that predicts a rebound in prices after a decline. The report cited sources from Binance and a crypto data website, and stated that the raw models and data were saved.
A data analysis from the source https://runway.adityaperswal.workers.dev/data/crypto-bars found that from January 1 to September 4, 2026, the net natural-exit samples for the cryptocurrencies XRP, LINK, UNI, and PAXG were 6, 14, 13, and 8, respectively. Using 20,000 independent and identically distributed resamples with a seed of 2026090502, the mean trade 95% intervals were calculated as XRP [-1.194, 18.765]%, LINK [-1.726, 2.184]%, UNI [-2.933, 2.634]%, and PAXG [-1.402, 0.419]%. This analysis assumes that the trades are independent, but the current selection of trades and market conditions may limit the interpretation of the results. The frequencies of positive resamples were 94.375% for XRP, 51.585% for LINK, 38.65% for UNI, and 13.8% for PAXG, but these are not posterior probabilities. A daily ordinary least squares analysis on BTC with an intercept found betas of 1.237, 1.176, 1.181, and 0.299 for XRP, LINK, UNI, and PAXG, respectively, with correlations of 0.871, 0.874, 0.677, and 0.446. The sample size was 247 for each cryptocurrency except PAXG, which had a sample size of 204. The similar betas for XRP and LINK weaken the narrative of diversification. The raw binary mean-payoff quarter Kelly notional formula gave capital fractions of 3.889, 0.281, -0.468, and -7.550, which are clearly unstable and constrained before caps. Since the lower mean bounds are negative, the authorized allocation is 0. The premises of a positive XRP return and a win rate of 66.67% depend on only 6 trades, and the strongest bull case is a large payoff, but no precise population edge has been established. There is a need for prospective fixed rule evidence and tested venue exits, and one should not pick the best lookback from the inspected holdout. The UNI terminal open mark was excluded from payoffs rather than counted as a winner.
A news scan found the data from a crypto market analysis. The data, available at https://runway.adityaperswal.workers.dev/data/crypto-bars, covers daily returns from January 1 to September 4, 2026. It shows that the correlation between Bitcoin (BTC) and Ethereum (ETH) is 0.9124, between BTC and Solana (SOL) is 0.8805, and between ETH and SOL is 0.8801. This means that these currencies tend to move together in the market. The beta values, which measure volatility, are 1.254 for ETH and 1.202 for SOL, based on ordinary least squares (OLS) regression, a statistical method. The daily standard deviation, a measure of risk, is 2.452% for BTC, 3.370% for ETH, and 3.347% for SOL. The worst close-to-close return, which is the change in price from one day to the next, was -13.986% for BTC, -14.890% for ETH, and -14.931% for SOL. Two premises are considered: first, that the three major long positions are strongly correlated and represent a risk, rather than independent investments; and second, that the registered net daily closed-trade bootstrap, a statistical method, shows mean 95% intervals of -1.424% to 1.323% for BTC, -2.670% to 0.678% for ETH, and -1.213% to 6.606% for SOL. For SOL, there were 12 trades, with a 50% win rate, a mean win of 6.123%, and a mean loss of 2.270%. The naive quarter-Kelly, a betting strategy, is 0.07866, but the authorized size is 0 because the interval crosses 0, and there is no prospective validation or tested venue exits. The bootstrap-positive fraction of 0.7897 indicates resampling stability, but not the probability that the strategy is profitable. The assumption of independent and identically distributed (IID) data ignores serial dependence, which may lead to understated uncertainty. All hourly net mean intervals were entirely negative, ranging from -0.611% to -0.443% for BTC, -0.609% to -0.346% for ETH, and -0.682% to -0.465% for SOL. Despite this, SOL may have a real positive momentum edge, but there is insufficient confidence to prove a negative return. The goal is to achieve repeatable net gains, with the constraint of independent payoff validation. The methods and results are available in the return-statistics.json, bootstrap.json, and risk_analysis.py files, but no predictive regression is claimed.