Regarding my recent "crazy" market cap projection of a $25B $Pippin valuation (and the accompanying Elliot Wave simulation), I analyzed thousands of lines of pricing data across various timeframes using Python with custom libraries.

Even with a potential margin of error of 15%, achieving an 85% win rate for something this new demonstrates Python’s effectiveness as a tool for sales and spend projections.

For the Elliot Wave simulation, I identified peaks and valleys and calculated Fibonacci retracements. This was achieved using Pandas for data manipulation and SciPy for peak/valley detection, enabling precise modeling of market cycles and retracement levels.

For predictive price modeling, I leveraged ARIMA (from statsmodels) for time-series analysis and price prediction. Additionally, I utilized XGBoost to generate data-driven forecasts by incorporating multiple technical indicators.

When a particular market cap is reached and Retail has piled in, they will not be buying because of the tech. WE bought because of the tech. THEY bought because FOMO and greed run deep—but by then, they were already late to the party.
#Pippin