I don't have long, so I'll keep it short. I spent the majority of my morning retrieving tick data for Pfizer and compiling it into one location, I think I ended up with about 150,000 data points. I then took the first 40,000 data points and fitted them with logical statements:
- If my data point increased from the previous it get's a one, otherwise zero
- If my data point decreased from the previous it get's a one, otherwise a zero
- If my data point remains the same from the previous it get's a one, otherwise a zero
From here I was able to find the probability of increase, decrease, and no change. As one would assume the probabilities for increase and decrease were incredibly close (I think they deviated by .0001% most the time -- I'm not sure, my data is on a computer across town).
This created a nice reference point; since my long-run probability of an increase is X and my short-run probability of an increase is <X; this indicates that my short-run average would begin to increase in order to bring the my long-run average back to X. That is, every time the probability of a price increase fell below the long-run average, we begin to see future probabilities increase in order to maintain that long-run average. And that's exactly what happened.
Every time my most recent 10,000 data points fell below the long-run average I placed a buy marker and sold on the next tick. I simulated this for 7 days (September 6 - September 14). The results were quite pleasing:
- Each day resulted in a gain
- After 7 days I ended up with 103.85% of my starting capital
This has it's limitations though:
- This process depends on speed, each buy must be on that tick and each sell on the very next; I'm not great with computers -- actually, I'm not even okay with computers -- so I don't know the power necessary to fulfill these requirements.
- Fees will destroy me. I either need large sums of money to reduce my fees per share or I need to find a brokerage that takes a percentage of my gains. Preferably the former.
I'm also going to need to add in safety nets, here's what I'm thinking:
- Fit my data to a distribution curve (don't know which yet) and find my standard deviations. If my standard deviation falls below 1 to the left it becomes a buy.
- Create a conditional probability P( increase on next tick | current tick falls below one standard deviation). From here I can compare across companies and pick the one that gives me the greatest probability of an increase.
I still have work to do on this, but it's an interesting hypothesis none the less. Further testing is required, but beforehand I must a) gather more data b) apply this hypothesis to more than one company ( i.e. all the S&P 500) and c) write a program that can run my simulation.
You will see more of this in my coming posts.