Monte Carlo Simulation and Python 1 - Intro

Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 In the monte carlo simulation with Python series, we test various betting strategies. A simple 50/50 strategy, a martingale strategy, and the d'alembert strategy. We use the monte carlo simulator to calculate possible paths, as well as to calculate preferred variables to use including wager size, how many wagers, and more. There are many purposes for a monte carlo simulator. Some people use them as a form of brute force to solve complex mathematical equations. A popular example used is to have a monte carlo simulator solve for pi. In our case, we are using the Monte Carlo simulator to account for randomness and the degree of risk associated with a betting strategy. In the world of stock trading and investing, people can use the Monte Carlo simulator to test a given strategy's risk. It used to be very much the case that only performance was considered, for the most part, to decide on a trader's value. Only until recently has the paradigm shifted to consider a strategy's risk more closely. Through this series, you will be able to see just how much random variability can affect the outcome, regardless of how "good" or "bad" a strategy might have been. http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6

Comments

1. Nice tutorial, thanks dude. Do you also have a tutorial on optimization?
2. Thanks, sentdex.
   great video!
3. Can you help me identify whether monte carlo would be a decent approach to solve this problem:I have the performance of a metric per day (down to a 30 minute interval level) which means I can account for weekdays, day of month, etc. It's Jan 20 right now so there are 11 days left in the month. Based on the current actual performance for the month and the historical data from previous months (accounting for the labels I mentioned previously), is there a way to estimate what the end of month performance result will be? Example: Number of interactions and average interaction length. Can I build something to accurately predict the end of month average interaction length knowing I have X number of days left (some of those days being weekends, maybe performance at the end of the month is better, etc depending on historical data, maybe there has been improvement in the latest month which should carry some weight)? Would I use monte carlo to give me a range of potential results?
4. Hi, are you interested in doing a hierarchical clustering analysis series?
5. yo this guy had me at options and scottrade
6. Monte python
7. i am also just seeing a white screen- thx
8. I just see a white screen on the videos.
9. these video series are one of the best videos I've seen on internet recently. I really thank you! 
10. great video man
11. Hello Harrison, I can see the other videos except this, what will happen?. Very good material. Greetings from Colombia.
12. Hi sentdex, Thanks for sharing such great videos. Could provide the source code?  It's a little bit hard to follow your code speed.
13. Woooow , your videos is very interesting , your videos pushing me forward and every video give me an extra reason to learn python , and I ve started learning it , but Python 3.4 , and now i am on chapter " Control Flow " wich is contain " if , elif ...." , and then i will see Your playlist on  "Matplotlib " , then charting playlist " here i hope to get what i want from the beginning " , then Machine learning Playlist , and finally this , Note : I will keep following You , this is Unique channel , good luck buddy
14. Harrison are you going to make a video of the assumptions for monte carlo simulation and are you going to use Scipy for the probability distributions?
15. I'm also share this common interest (trade, programming, python)
    
    very interesting topic, Pythonic way to approach this i'll be great!
    
    Thank YOU! Harrison!
16. R or Python