Stats

A news scan found the following stock prices as of the September 3 close: ADBE was 285.75, up 9.80% over the last 20 days, and above its 20-day average price, or SMA20, of 274.05 and its 50-day average price, or SMA50, of 247.39, with a 14-day average trading range, or ATR14, of 3.44%. KR was 58.51, up 2.08% over the last 20 days, and only slightly above its SMA20 of 57.26 and SMA50 of 57.68, with an ATR14 of 2.28%. GME was 19.23, unchanged over the last 20 days, and below its SMA50 of 20.30, with an ATR14 of 2.25%. IONS was 58.13, below its SMA20 of 59.41 and SMA50 of 61.64. MIRM was 100.63, above its SMA20 of 98.99 but below its SMA50 of 109.39. None of these stocks offer a new, clear opportunity to buy or sell now.

A trading hypothesis was tested: buy a stock 10 days before the company announces its earnings and sell at the previous day's closing price. The test used daily closing prices from Alpaca, and 14 events verified from primary sources between March 2025 and June 2026 were analyzed, subtracting 10 basis points for round-trip costs, with the last event per symbol set aside. The full sample of events resulted in a compounded gain of 14.8%, with a maximum loss of 11.8%, a win rate of 42.9%, an average gain of 7.69%, and an average loss of 3.62%. The in-sample test of 11 events showed a gain of 17.7%, while the 3-event holdout test resulted in a loss of 2.45% with a win rate of 33.3%. The results were driven by a few large gains in stocks like ADBE and GME, but the strategy does not appear to have a robust repeatable edge, leading to the rejection of this generic run-up entry rule.

The idea is that in a trending or volatile cryptocurrency market, a simple strategy called a Donchian breakout, which involves buying when the price reaches a new high over a certain number of days and selling when the signal reverses, can capture gains while avoiding most of a sustained loss, performing better than a buy-and-hold approach on a risk-adjusted basis, even if it lags behind a strong uptrend. The method used was a backtest, which is a way of testing a strategy using past data, with a daily timeframe, using the symbols BTC/USD, ETH/USD, and SOL/USD, and looking at different time periods of 180 days, 365 days, and 730 days. The results showed that over 365 days, BTC had a gain of 8.1% compared to a buy-and-hold loss of 30.7%, ETH had a loss of 14.7% compared to a buy-and-hold loss of 44.6%, and SOL had a gain of 31.4% compared to a buy-and-hold loss of 52.7%. Over 730 days, BTC had a gain of 26.2% compared to a buy-and-hold gain of 30.5%, ETH had a gain of 24.7% compared to a buy-and-hold loss of 5.7%, and SOL had a gain of 69.9% compared to a buy-and-hold loss of 26.7%. In a strong uptrend over 180 days, BTC had a gain of 2.9% compared to a buy-and-hold gain of 13.2%, ETH had a loss of 12.2% compared to a buy-and-hold gain of 21.0%, and SOL had a gain of 34.6% compared to a buy-and-hold gain of 16.9%. The lookback period, which is the number of days used to determine the breakout, of 20-30 days was found to be robust, and the stop and target percentages of 8-12% and 15-25% had little effect on BTC, but a wider target of 25% helped SOL, with a gain of 32.5% compared to 12.2% at a 15% target. As of September 2, 2026, all three cryptocurrencies are flat with no signal, and to trigger a new long position, BTC needs to reach a price of around 81,450, which is a 20-day high, currently at 77,120, a gain of 5.6%, ETH needs to reach a price of around 2,566, currently at 2,394, a gain of 7.2%, and SOL needs to reach a price of around 110.5, currently at 99.0, a gain of 11.7%.

The idea is to use a classic time-series momentum strategy, which is a method to make investment decisions based on past price movements, as documented by researchers Moskowitz, Ooi, and Pedersen at AQR. This strategy, called TSMOM, involves buying when the short-term trend, measured by a 50-day simple moving average, or SMA, which is a calculation of the average price of an asset over a certain period, crosses above the long-term trend, measured by a 200-day SMA. The goal is to capture major trends in the prices of Bitcoin and Ethereum. To test this idea, a computer program called backtest.js was used to simulate the strategy on the prices of Bitcoin and Ethereum over 730 days, without considering any costs or losses from buying and selling. The results were: for Bitcoin, a loss of 20.6% compared to a gain of 31.0% from simply buying and holding, with a maximum decline of 24.5% and no winning trades. For Ethereum, a gain of 21.5% compared to a loss of 5.0% from buying and holding, but this result is not meaningful because it was based on only one trade over 731 days.

The idea is that using a statistical method called log-spread z-score to trade pairs of assets and using momentum and mean-reversion strategies on single assets in the cryptocurrency market can produce positive returns. The method used was to test pairs of Ethereum and Solana, and Bitcoin, using a computer program called backtest.js with specific parameters: looking back 20 days, entering a trade at 2 standard deviations, exiting at 0.5 standard deviations, over 365 days, which requires both buying and selling the spread. For Bitcoin momentum, the test looked back 20 days over 365 days. For Bitcoin mean-reversion, the test looked back 10 to 20 days, entering a trade at 1.5 to 2 standard deviations, over 180 to 365 days. The result for pairs of Ethereum and Solana was a 17.9% return, compared to buying and holding which resulted in a -48.1% return, with a maximum drawdown of 14.6% and a win rate of 63.6%, which looks good but cannot be implemented because the trading platform does not allow selling short. The result for Bitcoin momentum was a -5.1% return, which is better than buying and holding which resulted in a -30.3% return, but still a loss. The result for Bitcoin mean-reversion was a return of -7.4% to -9.5% across different parameters, which is not a positive return and does not perform better than buying and holding.

The idea is to use a strategy called cross-sectional momentum, which means buying the top performers and selling the worst ones. This strategy, also known as Jegadeesh-Titman or Faber sector rotation, involves buying the top 3 out of 10 investments based on their performance over the past 60 to 120 days and rebalancing every month. The goal is to beat a simple buy and hold approach where all investments are given equal weight. The method used to test this idea was to run a backtest on a group of stocks including AAPL, MSFT, NVDA, GOOGL, AMZN, META, AVGO, TSLA, JPM, and XOM over different time periods, as well as on a group of sector ETFs including XLE, XLF, XLK, XLV, XLI, XLU, XLP, XLY, XLB, and XLRE with the same parameters. The result for the mega-cap tech stocks was a return of -9.9% over 365 days when using a 90-day lookback period, compared to a +6.8% return for a buy and hold approach. Using a 60-day lookback period resulted in a -5.0% return, compared to a +9.2% return for buy and hold. Even when using a 120-day lookback period over 700 days, the return was +40.5%, which still trailed the +52.6% return for buy and hold. For the sector ETFs, the results were also underwhelming, with returns ranging from -45.7% to -3.5%, compared to buy and hold returns ranging from -12.0% to +9.9%. The strategy underperformed a buy and hold approach in every time period tested, suggesting that concentrating on the top 3 performers in a correlated group of investments adds costs without capturing the benefits of diversification.

The idea was to test if buying the top 2 of 6 major cryptocurrencies (BTC, ETH, SOL, DOGE, XRP, LTC) based on their past performance would work better than buying and holding them. This is called cross-sectional momentum, a type of investment strategy. The test used a computer program called backtest.js to look at how this strategy would have worked over 365 days and 180 days. The program looked at the daily prices of these cryptocurrencies from Alpaca. The results showed that over 365 days, this strategy would have lost 61.1% of the investment, which is worse than buying and holding, which would have lost 53.7%. Over 180 days, the strategy would have lost 1.72%, which is also worse than buying and holding, which would have gained 5.12%. The strategy also had high costs from frequently switching between investments, and it did not work well when the prices of these cryptocurrencies were moving together in a downward trend. The maximum loss at any point was 71.8% over 365 days and 33.8% over 180 days. The strategy was successful 40.4% of the time over 365 days and 47.6% of the time over 180 days.