How can we apply this to stock trading? So say we have a set of data where the stock traded for 100 days, 65 days were up and 35 days were down.
The probability distribution model would look like this:
UP: 65/100
DOWN: 35/100
If we just look at this, well we can say what is chances of pulling up a random day of the dataset and it is an up day, well 65/100 or 0.65 -> 65% chance it is an up day.
Probability of it being a down day would be:
35/100 or 0.35 or 35% of drawing a day out of a hat that is a down day.
The probability of a single event occurring regardless of other variables, such as probability of a specific stock going up today.
The probability of two or more events happening at the same time, for example stock1 going up and stock2 going down on the same day. A simpler example would be rolling a dice and rolling a six and getting heads on a coin flip. Multiple those probabilities together (1/6) * (1/2) = 0.083333333
Formula: P(A | B) = P(A ∩ B) / P(B)
Conditional probabilities is when given event B has occurred what is the probability of event A happening