classicvalues / blackjack-simulator

BlackJack-Simulator written in Python with OMEGA II Count

BlackJack-Simulator with OMEGA II Card Counting

Flexible BlackJack-Simulator written in Python. It takes a given basic strategy as input (defined in a .csv-file) and simulates that strategy over a given amount of time. The simulator also counts cards sticking to the OMEGA II Count, which basically gives every card some value. Depending on the current count the bet size gets adjusted.

Running

python BlackJack.py strategy/BasicStrategy.csv

Omega II Count:

2	3	4	5	6	7	8	9	10	J	Q	K	A
+1	+1	+2	+2	+2	+1	0	-1	-2	-2	-2	-2	0

So, for example if there is a player-favorable count like +20 by 2 decks remaining, the simulator bets the standard bet times the specified BET_SPREAD.

Definition of Terms

The simulator involves several concepts related to Blackjack game play:

• A Hand is a single hand of Blackjack, consisting of two or more cards
• A Round is single round of Blackjack, in which one or more players play their hands against the dealer's hand
• A Shoe consists of multiple card decks consisting of SHOE_SIZE times 52 cards
• A Game is a sequence of Rounds that starts with a fresh Shoe and ends when the Shoe gets reshuffled

Result

The simulator provides the net winnings result per game played and an overall result summing up all the game results. The following output for example indicates, that in game no. 67 the simulated player won 18 hands more than he lost. On the other hand in game no. 68 the simulator lost 120 hands more than he won.

 ...
 WIN for Game no. 67: 18.000000
 WIN for Game no. 68: -120.000000
 ...

This graph displays every game with its total won or lost hands. You can see that in some rare games about 60 more hands are lost/won than won/lost. If the expectation is positive, you have developed a Winning BlackJack Strategy, which is the case for the provided BasicStrategy plus the OMEGA II count.

This graph displays the development of the count for each game. You can see that the card count in rare cases even exceeds 40 and is on average as you would expect 0.

Gaming Rules

The simulator plays with the following casino rules:

• Dealer stands on soft 17
• Double down after splitting hands is allowed
• No BlackJack after splitting hands
• 3 times 7 is counted as a BlackJack

Configuration Variables

Variable	Description
GAMES	The number of games that should be played
ROUNDS_PER_GAME	The number of rounds that should be played per game (may cover multiple shoes)
SHOE_SIZE	The number of decks that are used
SHOE_PENETRATION	Indicates the percentage of cards that still remain in the shoe, when the shoe gets reshuffled
BET_SPREAD	The multiplier for the bet size in a player favorable counting situation

Sample Configuration

GAMES = 1
ROUNDS = 10
SHOE_SIZE = 8
SHOE_PENETRATION = 0.2 # reshuffle after 80% of all cards are played
BET_SPREAD = 20.0 # Bet 20-times the money if the count is player-favorable

Strategy

Any strategy can be fed into the simulator as a .csv file. The default strategy that comes with this simulator looks like the following:

• The first column shows both player's cards added up
• The first row shows the dealers up-card
• S ... Stand
• H ... Hit
• Sr ... Surrender
• D ... Double Down
• P ... Split

Note on the shuffle method used

The shuffle method used is the default random.shuffle() which comes with a warning :
"[if] the total number of permutations of x is larger than the period of most random number generators, [then] most permutations of a long sequence can never be generated."
https://docs.python.org/2/library/random.html
Hopefully :
"Python uses the Mersenne Twister as the core generator. It produces 53-bit precision floats and has a period of 2**19937-1" Which means that a list of over ~~2080 elements would never see all its permutations, even if it got shuffled an infinite number of times. 8x52 = 416 is low enough to ignore this problem.