Angular Momentum #1

Sat, 02 Nov 2024 13:51:04 GMT

![Image](https://upload.cafenono.com/image/slashpagePost/20241102/225312_W59EwhWzEMUUxnP3lc?q=80&s=1280x180&t=outside&f=webp)

## Scientific Method

1. **Question:** Can 1 day forward return be predict using statistical indicators of volume, price and variation?

2. **Hypothesis:** There's a relation between short historical data and future data linked by "momentum".

3. **Experiment:** The historical data was downloaded via Python. Tech indicators was calculated and added to model using rolling window method. Dataframe was sorted by 1 day forward return. Grouped by 9 (5+1+5) bins with equal amounts per group. And summurized by simple mean.

4. **Analysis:** A new result never seen before was discovered. There's not a linear relation from -5 to +5 level as expected. But a parabolic relation, subtly indicating 3 points of convergence: 2 in extremes in 1 in the middle.

5. **Conclusion:** Buy ETF Indice on open market and sell on close when stdeviation of previous returns is high don't look to be a good idea, because can lead to either very high positive variation or very high negative one. In other words, seems not to exist a "Linear Momentum" in short timeframe, but yes something similar to "Angular Momentum". When market is turbulent, looks like it will continue to be turbulent for D+1, with high bullish or bearish "marubozus". In that case, one-dimensional linear operations (buy or sell) seem to make less sense than two-dimensional operations (volatility options), such as Iron Condors and Butterflies strategies.

## Technical Variables

🆔 **Ticker:** SPY (S&P 500 ETF)

⏱️ **TFrame:** Daily

🧮 **Formulas:** Maximum, median, geomean, geostd, minimum and zscore

📐 **Windows:** 3, 5, 10, 15, 20, 30, 40, 50

📚 **Libraries:** Numpy, Pandas

💿 **Source:** YFinance

📈 **Until:** 30 Years Data