Trying to value Bitcoin is complicated and numberless. In a simple experiment, I wanted to see what I could do with Bitcoin’s historical data from YahooFinance! in a couple of hours.
To my surprise, I learned much more about Bitcoin’s price behavior than I initially thought. You can download historical Bitcoin data here. You can also view my simple Excel model here.
Using a heatmap, I realized that April, May, and December are the best months to purchase and hold Bitcoin. Furthermore, Monday and Saturdays are the best weekdays to purchase Bitcoin.
Furthermore, Bitcoin likes to increase prices until it hits a 10% cumulative gain. After this, the chance that Bitcoin dips in price increases exponentially.
Lastly, using historical bitcoin prices leads to pretty insane forecasts. In some cases, Bitcoin hit $1,000,000 per coin using a Monty Carlo simulation. You can see the charts and graphs below:
I applied a simple heatmap to see which days and months average the best returns for Bitcoin. The highest months were April, May, and December. The lowest months were January, March, and September.
The bar plot above shows us the average prices by month. For example, a safe price to buy Bitcoin might be around $5,000. However, between February and April, you should aim for a buy price of $3,500 instead.
I applied a simple price analysis here to see when cumulative gains tend to stop. The blue line represents the actual frequencies of dips by percent gain, while the orange line represents a perfect distribution of dips. A case for holding or buying Bitcoin occurs when the blue line crosses the orange line. A case to sell occurs when the opposite is true.
My favorite chart is the one above. It is a Monte Carlo simulation that predicts future prices using random numbers and historical data. I ran three predictions. You can see the ridiculous forecast of Bitcoin price and enjoy it for what it is.
This is an extremely limit experiment. You should do much more research before using this to sell or buy. Still, I hope you enjoyed the simple analysis and apply this to stocks and other investment vehicles.