I am working on stock market analysis and prediction using machine learning methods, especially with reinforcement learning. I am trying to predict short, long and flat. (buy, hold, sell) . (any suggestion or material is appreciated),
currently, I am giving historical data into my agent and agent predict buy, sell or hold signal.
my question is how to measure stock quantity. e.g. if my model gives a buy signal, how to measure how much stock I should buy.
I think that should be a variable in your model. It may depend on how strong the buy signal is and it will probably depend on how many assets you have available to buy stocks. An individual will most likely not have the assets on hand to buy stocks that Berkshire Hathaway would. It is interesting to note that if large purchases are being made by Berkshire Hathaway the price actually changes when people find out or even if they suspect. That is a dynamic that you could ignore for smaller purchases.
It recently emerged on a large poker site that some players were possibly able to see all opponents cards as they played through exploiting a security vulnerability that was discovered.
A naïve cheater would win at an incredibly fast rate, and these cheats are caught very quickly usually, and if not caught quickly they are easy to detect through a quick scan through their hand histories.
The more difficult problem occurs when the cheater exhibits intelligence, bluffing in spots they are bound to be called in, calling river bets with the worst hands, the basic premise is that they lose pots on purpose to disguise their ability to see other players cards, and they win at a reasonably realistic rate.
Given:
A data set of millions of verified and complete information hand histories
Theoretical unlimited computer power
Assume the game No Limit Hold'em, although suggestions on Omaha or limit poker may be beneficial
How could we reasonably accurately classify these cheaters? The original 2+2 thread appeals for ideas, and I thought that the SO community might have some useful suggestions.
It's an interesting problem also because it is current, and has real application in bettering the world if someone finds a creative solution, as there is a good chance genuine players will have funds refunded to them when identified cheaters are discovered.
Plot V$PIP versus winrate of all players with a statistically significant #hands played. You should see outliers with naked eye. I think that's the basic thing to do first.
Then you can plot WTSD vs winrate, winrate at showdown vs winrate without showdown, %won at showdown vs VPIP.
The stats you choose must be significant statistically. If you know poker, the above choices make sense.
This is not a job for a machine, outliers are detected by eye.
EDIT: Omaha is much tougher, since it is really variant. There are cases of unbelievable streaks made by weak players who were not cheating.
I hate to be so blunt, but all the answers on this page with the exception of #Erwin Smout's are worthless.
Statistical analysis is a joke for identifying poker cheats
I realize the question allows there to be millions of hands worth of history available to the system. I'm sure there are players with hand histories this large, hell, I've probably played this many online hands. But I've also been playing online for over 10 years. Thats not a small amount of time, and it is my understanding that two conflicting things are true when it comes to identifying online poker cheaters: it needs to happen in a small amount of time, and like any good thief, an online poker cheat is going to take his stash elsewhere immediately after the taking.
There was a great example of the variance in poker in this paper which was generated by matching an always raise player versus an always call player (page 13 of the PDF). Over the course of 100,000 hands, wayyyy more than I think most people would be willing to play against someone who could see their cards, the always call player won on average .026 small blinds per hand. I know this does not sound like much, but assuming stakes of $5-10, that comes out to $6,500. Maybe someone can help me find the link, but the measured professional win rate is less not too much larger than this. Please note, NEITHER of these players was cheating, and the statistically expected difference over this number of hands is significantly less than what actually transpired.
What online poker players need to understand
Poker is gambling. It is a game of skill, because some players are able to elicit more information from their opponents than their opponents are able to gather, and that extra information is often as useful as seeing other peoples cards. Even players who are better players than their typical opponents, will end up long term losers. If you do not understand this, you're just searching for witches with statistics in the arbitrarily small number of hands you'll be playing against any opponent.
What can be done?
Keeping in mind the question states that cheaters are able to see the other players cards, you don't need statistical analysis to identify them. There are only three ways in which that is possible.
First is that the server is sending the information intentionally to clients which is an obvious security issue and should not be implemented (IMO, even for moderators). If a site was found allowing this to happen, it is the player's responsibility to move their funds elsewhere, or refuse to play on the site until that terrible design decision is rectified. It should also be the responsibility of the sites to inform their players of the exact steps that take place during hands played on the site so they have that to make their decision on when choosing a site in the first place. Security by obscurity is unpermitable. As for catching the thieves, this information should be sitting in log files on their servers, which should be regularly audited for this type of behavior.
Second is that the user has hacked the poker server and they would know about that in hurry, or else once it is exposed, it is again players responsibility to determine where to play. In this case, the cheater can be prosecuted in most countries.
Lastly, it is possible the dealing algorithm has been cracked. This one was a major problem in the past with companies that used naive methods to deal hands, but most of the major shops solved this problem by taking random inputs from players logged into their system as well as using entropy generating hardware to seed their random number generator. Thats not to say it cannot be cracked however. If this is the case, the only option is for the company to engineer a new random number generator.
Well. IT people get fascinated by all kinds of wrong question.
A better question is "how is cheating even possible ?". There is no need what so ever to send the opponent's hands over the wire until at showdown. If that data isn't sent to the client, then how could they cheat ?
They'd need to break into the server. Don't tell me that isn't preventable.
I think if they cheat intelligent, so with winning not too much rounds, it won't be detectable. I don't believe you could see the difference between luck and cheating here.
But I would like to know at which online poker provider the cheating is possible. Because I can't imagine a way how to do this, if the poker software is coded properly. If I was asked to program an online poker software, The users wouldn't be able to see the opponents cards, because there is no way he could get this information. And this is how I would do this.
Every connection between users and server is encrypted
no communication between users, the users can only talk to the server.
The server tells every user only the cards the user should see, and no other cards, unless the round is finished and the users open their cards.
The only way the users could cheat here is, you get together with other players, or impersonate multiple players with different accounts and accessing IPs, and open another channel to communicate between the players. This way the group has a big advantage because they know more than their own cards, but there's still no way they can see other cards. And because it's now a group that is cheating it is even more harder to detect it, because they can share their earnings with multiple players, and this group could even have a player that looses more than (s)he gains and still win overall.
I doubt you can say with any certainty if someone is cheating or if they are just good at Poker, past a certain point.
You could however narrow the candidates who you think might be cheating, by looking at the users who over your time period benefited overall. This will remove the vast majority of users, allowing you to focus your resources better. (This of course will include users who are skilled at Poker.).
Once you've done that, you can compare the history of play from while the cheat was possible to the history afterwards or before, and see if the users success decreases or increases.
That should give you a list of users who you need to investigate more carefully, possibly by analyzing specific games.
Enjoy, it's a nice problem.
For all of you expressing disbelief that this is even possible: the community on the poker forums linked in OP were similarly awestruck, but the site in question has confirmed that such a security vulnerability was present. Quite simply, the site was using very basic and insecure crypto to transmit hole card data to its players. Theoretically, it would have been possible for anyone aware of this to intercept transmissions from the site to a specific victim (eg. by being physically nearby and intercepting wireless data), and to cheat that player using the intercepted knowledge.
The question is about how to detect whether this vulnerability was actually exploited (before it was fixed), and if so by whom, given the resources outlined.
Oh, and also some of you seem to be assuming we're talking about a hypothetical scenario, and/or play-money poker; we're not. The site is real, the vulnerability was real, the investigation is really happening (see link in OP), and the games under investigation are real-money games with normal buyins of $200 and above.
I'm by no means a data-mining expert, and my grasp of statistical analysis of large data sets is pretty weak as well (and I'm not very good at poker, even though I love it) so take everything I say here with a grain of salt.
Weed out the junk data. You are going to only really care about players that fit into two categories: (1) players who win more hands than they lose, (2) players who win more money than they lose. Who cares about a cheater who loses a lot? Heh.
With this paired down list of players to actually analyze, I would take a look at their style of play. Assuming you have a lot of historical data, I would build a player skill profile and attempt to normalize their betting strategy. As a poor poker player, I normally will back up weaker cards that no decent player would back simply because they feel good. For example, any time I am dealt a face card with another low card (2, 3, 4, 5), if they're suited, I'll often ALWAYS call any bets made by other players before the turn, even though this strategy is not very successful. Pre-turn raises above the Big Blind often indicate a player has a pocket pair, yet my love of playing won't let me fold a suited hand pre-flop.
So for me, your analysis of my play would say that me matching aggressive calls pre-flop when I have anything suited would be normal. But a different player who only occasionally calls large pre-flop bets would be an indication that something might be out of whack.
I don't know what sort of system you'd need to build to make a profile of different users styles of play, but I imagine you could use some computer learning algorithms to "learn" a person's style of play with pretty decent accuracy.
You mentioned that a smart user would throw hands to minimize his appearance as a cheater. I think this is a GREAT opportunity for more profiling. Would an experienced, winning player play through an awful hand? Probably not, ever. If I was dealt a 4S, 7H, and saw 9D, JC, AH on the flop, I would know that my chances of winning were really, really small. It also tells us that the cards given on the flop aren't very strong for anyone, so anyone at the table betting probably has a Jack or Ace paired, two pair, or three of a kind. Since you know your 4S, 7H is worthless, you'd either bet hard to bluff the pot or fold outright. Not very many good players (who would have been found in your winning players shortened list) would ever stick around on a hand like that.
Anyway, those are the things I've thought of. Now actually implementing them, I have no idea where to even begin so I'm afraid I can't be of much help there. This is a very interesting academic problem though, so please do us a favor and keep us informed of what you end up going with. If you want to take this conversation offline, feel free to email me at stackoverflow#ericharrison.info.
Could you not look for simple indicators initially before trying to do anything too complex??
i.e.. PreFlop : A player folds pocket kings with no raise before him and someone else had pocket Aces..
This MIGHT be indicative of the player knowing his starting KINGS (pretty good) is not as good as someone elses pocket ACES .. however that's assuming he makes the decision pre-flop and not post flop.. depends really..
Ignore this, just thinking out loud..
To be perfectly honest, I'd doubt very much that the players who could see opponents hands were random. There must be some sort of cross over in the code that generates the card view that was selecting some users but not others. I would recommend running tests on this code and trying to find a trend in the "viewers" and "non-viewers". If you find a strong trend, then the trend could be applied to the actual dataset too see which users, or which hands or which whatever was generating the code fault.
The answer to your question is simple. There is no way to detect that type of cheater with just hand histories. You need the information that is not public in order to correlate multiple characteristic's to find a suspected cheater.
Ohh yea, and obviously the companies that provide these games do everything possible to setup shop in a low tax, non-regulated country. Until they are regulated and enforce strict code compliance and testing this will continue to happen.
the most likely cheating situation would seem to be people working together. Three guys at same table knowing each others cards should be able to make some betting adjustments that would allow the pool of betters to come out ahead.
What stops are in place to prevent collusion?
I'm looking to build an AI system to "pick" a fantasy football team. I have only basic knowledge of AI techniques (especially when it comes to game theory), so I am looking for advice on what techniques could be used to accomplish this and pointers to some reading materials.
I am aware that this may be a very difficult or maybe even impossible task for AI to accurately complete: however I am not too concerned on the accuracy, rather I am interested in learning some AI and this seems like a fun way to apply it.
Some basic facts about the game:
A team of 14 players must be picked
There is a limit on the total cost of players picked
The players picked must adhere to a certain configuration (there must always be one goalkeeper, at least two defenders, one midfielder and one forward)
The team may be altered on a weekly basis but removing/adding more than one player a week will inccur a penalty
P.S. I have stats on every match played in last season, could this be used to train the AI system?
This is interesting.
So if you didn't really care about accuracy at all, you could just come up with some heuristic for the quality of a team. For instance, assign a point value to each player and then try to maximize it using dynamic programming. Something like: http://www.cse.unl.edu/~goddard/Courses/CSCE310J/Lectures/Lecture8-DynamicProgramming.pdf
This would be similar to the knapsack problem.
Technically this is AI since a computer is deciding something but maybe not what you had in mind.
You sound like you want a learning AI (http://en.wikipedia.org/wiki/Machine_learning) which is an interesting field. Here's how you can approach the problem.
Define your inputs. Right now you have last years data. You'll probably want data on many years. Also, you might be able to include the ranking of pundits, maybe a bunch of magazines rank players or something, that seems useful as well.
Take your inputs and feed them into some machine learning algorithm for each season. Wikipedia will help you out there.
Essentially, for each season you'll want to feed in your data, have your AI pick a team, and then rate the performance of the team based on the seasons results.
Keep doing this and maybe your bot will get better at picking teams, and you can apply to this year's data.
(If you only have last year's data, it's okay to train the algorithm with just that but your AI will probably be over trained on that one set and won't be as accurate.)
This was just a sketch of how it might look. For a romp into AI, this problem is probably pretty hard so don't feel disheartened if it seems overwhelming at first.
There are two courses: "AI" and "AI in Games" both 15 students for 15 weeks.
I want to keep them motivated and creative.
I know I want some kind of competition (obvious for the latter course).
Maybe something like Marathon Match or ICFP.
I will need good visualization, so it would be great if it already exist.
One idea was to write AI for "Battle of Wesnoth", but I guess it's to diverse / boring.
Another game of Go. But that's too hard.
What are your ideas?
It will be work in groups of 3 students for 15 weeks.
MIT hosts a competition called BattleCode.
BattleCode, is a real-time strategy
game. Two teams of robots roam the
screen managing resources and
attacking each other with different
kinds of weapons. However, in
BattleCode each robot functions
autonomously; under the hood it runs a
Java virtual machine loaded up with
its team's player program. Robots in
the game communicate by radio and must
work together to accomplish their
goals.
Teams of one to four students enter
are given the BattleCode software and
a specification of the game rules.
Each team develops a player program,
which will be run by each of their
robots during BattleCode matches.
Contestants often use artificial
intelligence, pathfinding, distributed
algorithms, and/or network
communications to write their player.
At the final tournaments, the
autonomous players are pitted against
each other in a dramatic head-to-head
tournament. The final rounds of the
MIT tournament are played out in front
of a live audience, with the top teams
receiving cash prizes.
(source: mit.edu)
BattleCode in action.
You essentially are given the BattleCode software from MIT and your students can program the AI for their robots. They have a test suite so you can practice running your autonomous bots on your own in a practice arena. Towards the end of the semester they can enter in MIT's Open Tournament, where they compete with their software AI robots against schools all over the nation. Up to $40,000 is given away in cash and prizes as well as bragging rights for winning.
If you are looking to teach them about AI, Pathfinding, Swarm Intelligence, etc. I can't think of a more fun way.
May the best AI bot win!
Wii gesture recognition using hidden markov models.
I wouldn't count out Go. It's computationally hard for Go AI to compete with top human players, but the simple rules of Go (compared to Chess) make it a relatively easy game to write AI for. Your students' programs only need to compete against each other, not against Dan level human players. See An Introduction to the Computer Go Field and Associated Internet Resources for a lot of Go programming resources.
I think it's a good idea to select a theme both challenging enough that it can't be completely solved, yet allows the user to see the value of it in the real world and not so much a toy problem. My suggestion would thus be:
Word segmentation problem (e.g. convert "iamaboy" to "i'am a boy")
Word sense disambiguation (e.g. "The apple is nice to eat" - The apple is a fruit or a company?)
Optical character recognition
What I just list down is some of the more basic stuff of natural language processing. If your students is much more technically inclined, you can probably take it to the next level and let them tackle the problem of machine translation.
Empire, it's addictive as whatever and there are open source D versions (1 and 2) and a not quite free c++ version .