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A fully-featured blackjack game written in Go, integrated with Monte Carlo probability analysis, Kelly Criterion bankroll management, and intelligent decision recommendation systems.

• Real-time probability calculation: Based on Monte Carlo simulation (10,000 trials) to calculate winning probabilities
• Action win rate comparison: Analyzes expected win rates for hit, stand, double down, and other actions
• Optimal strategy recommendation: Automatically recommends the best decision based on basic strategy

• Intelligent betting suggestions: Provides scientific betting amount recommendations based on bankroll status
• Risk assessment: Real-time evaluation of current bankroll status and risk level
• Double down decision analysis: Evaluates expected ROI and risk-reward ratio for doubling down

• Basic strategy integration: Built-in professional blackjack basic strategy
• Real-time data analysis: Displays key indicators like bust probability, 21-point probability, etc.
• Entertainment cost estimation: Helps players understand expected entertainment costs

• Go 1.22+
• UTF-8 compatible terminal

go mod tidy

# Method 1: Run directly go run ./cmd # Method 2: Build and run go build -o blackjack ./cmd ./blackjack

• h / hit - Hit (take a card)
• s / stand - Stand
• d / double / doubledown - Double down
• q / quit - Quit game
• y / yes - Continue game
• n / no - End game

• 1 - Start game
• 2 - View game rules
• 3 - Exit program

Get your hand as close to 21 as possible without going over, while beating the dealer's hand.

• Number cards (2-10): Face value
• Face cards (J,Q,K): 10 points each
• Ace: Intelligently calculated as 1 or 11 (whichever is more favorable)

• Starting chips: 1000
• Betting options: 10, 25, 50, 100, 200 chips
• Smart betting: Automatic all-in option when chips are insufficient
• Payout rules:
  • 🏆 Regular win: 1:1
  • 🌟 Blackjack win: 3:2 (non-double situations)
  • 🤝 Push: Return original amount

• Trigger condition: Available on first two cards
• Chip requirement: Current chips ≥ current bet amount
• Game rule: Can only take one more card after doubling
• Payout adjustment: Blackjack pays 1:1 after doubling

1. Betting phase → Choose bet amount + bankroll management advice
2. Dealing phase → Player and dealer each get 2 cards (dealer has 1 hidden card)
3. Player turn → Choose hit/stand/double + probability analysis
4. Dealer turn → Dealer follows automatic rules
5. Settlement phase → Compare points and settle chips

The game displays detailed probability analysis each turn:

📊 Current Win Probability Analysis ──────────────────────────────────────── 🟢 Player Win Probability: 45.6% 🔴 Dealer Win Probability: 16.7% 🟡 Push Probability: 37.6% 📈 Detailed Analysis: 💥 Player Bust Probability: 85.1% 💥 Dealer Bust Probability: 22.7% 🎯 Player 21 Probability: 8.5% 🎯 Dealer 21 Probability: 6.3% 🎯 Action Win Rate Comparison: ✋ Stand: 45.8% ⭐ (Recommended) 👆 Hit: 41.9% ⚡ Double: 36.3% 

💰 Bankroll Management Advice: 📊 Recommended Bet: 15 chips (1.5% of bankroll) 💡 Your bankroll status is good, moderate betting recommended 🟢 Risk Status: Sufficient funds, controllable risk 🎮 Expected Entertainment Cost: 0.06% per hand 

💰 Kelly Criterion Double Analysis: ⚡ Recommend Double (Expected ROI: 18.0%) 🔴 Double Risk Level: High (Kelly fraction: 0.180) 

The game uses Monte Carlo simulation to calculate various probabilities:

1. Simulation count: 10,000 simulations per analysis
2. Strategy simulation: Player uses basic strategy, dealer follows fixed rules
3. Remaining deck: Based on current known cards and remaining deck
4. Statistical results: Statistics on frequency of various outcomes

f* = (bp - q) / b 

• f*: Optimal betting fraction
• b: Odds (1:1 or 3:2)
• p: Win probability
• q: Loss probability

1. Conservative factor: Use 25% of Kelly fraction to reduce risk
2. Tiered management: Different strategies based on bankroll status
3. Entertainment-oriented: Focus on bankroll management rather than strict expected returns

go-blackjack/ ├── cmd/ # 🚀 Program entry │ └── main.go ├── internal/ # 🔒 Internal modules │ ├── domain/ # 🎯 Domain layer - Core business logic │ │ └── entities/ │ │ ├── game.go # Game aggregate root │ │ ├── player.go # Player entity │ │ ├── dealer.go # Dealer entity │ │ ├── card.go # Card entity │ │ ├── deck.go # Deck entity │ │ ├── hand.go # Hand entity │ │ └── types.go # Type definitions │ ├── application/ # 🔄 Application layer - Use case orchestration │ │ ├── services/ │ │ │ ├── game.go # Game application service │ │ │ └── probability.go # Probability calculation service │ │ └── dtos/ │ │ └── game.go # Data transfer objects │ └── interfaces/ # 🖥️ Interface layer - User interaction │ └── cli/ │ ├── game.go # CLI handler │ └── display.go # Display service ├── go.mod # 📦 Dependency management ├── go.sum └── README.md # 📖 Project documentation 

• Responsibility: Core business logic and rules
• Characteristics: No external dependencies, pure business code
• Contains: Game entities, business rules, state management

// Game aggregate root - Unified game state management type Game struct { ID string Player *Player Dealer *Dealer Deck *Deck State GameState // State machine management RoundNumber int }

• Responsibility: Use case orchestration, domain object coordination
• Characteristics: Handles business processes, data transformation
• Contains: Application services, probability calculation, DTO objects

// Game application service - Orchestrates game use cases type GameApplicationService struct { game *entities.Game probabilityCalc *ProbabilityCalculator } // Probability calculator - Monte Carlo simulation type ProbabilityCalculator struct { trials int // Number of simulations rng *rand.Rand }

• Responsibility: User interaction, input/output handling
• Characteristics: Extensible for multiple interface implementations
• Contains: CLI handler, display service

// CLI game handler type GameHandler struct { gameService *services.GameApplicationService display *DisplayService }

User Input → CLI Handler → Application Service → Probability Calculator ↓ ↓ Domain Entities ← Application Service ← Monte Carlo Simulation ↓ CLI Display ← Kelly Formula Analysis 

type GameState int const ( StateWaitingToBet GameState = iota StatePlayerTurn StateDealerTurn StateGameOver )

Automatically calculates optimal Ace value (1 or 11) to ensure best possible hand value.

type ProbabilityResult struct { PlayerWinProbability float64 DealerWinProbability float64 PlayerBustProbability float64 ActionAnalysis *ActionAnalysis }

type KellyRecommendation struct { RecommendedBetAmount int RecommendedBetFraction float64 ShouldDouble bool RiskLevel string }

Strict business rule validation in the domain layer ensures game logic correctness.

• Multi-interface support: Easy to add web, mobile interfaces
• Feature extension: Easy to add split, surrender features
• Algorithm optimization: Supports different probability calculation methods

💰 Current Chips: 1000 Please select bet amount: 1. 10 chips 2. 25 chips 3. 50 chips 4. 100 chips 5. 200 chips 6. Exit game 💰 Bankroll Management Advice: 📊 Recommended Bet: 15 chips (1.5% of bankroll) 💡 Your bankroll status is good, moderate betting recommended 🟢 Risk Status: Sufficient funds, controllable risk 🎮 Expected Entertainment Cost: 0.06% per hand 

👨 Dealer Hand (first card hidden): 🂠 🃏A 👨 Player Hand (Value: 9): 🃏5 🃏4 ──────────────────────────────────────── 📊 Current Win Probability Analysis ──────────────────────────────────────── 🟢 Player Win Probability: 59.1% 🔴 Dealer Win Probability: 34.0% 🟡 Push Probability: 6.8% 📈 Detailed Analysis: 💥 Player Bust Probability: 0.0% 💥 Dealer Bust Probability: 44.4% 🎯 Player 21 Probability: 0.0% 🎯 Dealer 21 Probability: 8.7% 🎯 Action Win Rate Comparison: ✋ Stand: 44.2% 👆 Hit: 56.4% ⚡ Double: 59.0% ⭐ (Recommended) 🏆 Optimal Strategy Expected Win Rate: 59.0% 💰 Kelly Criterion Double Analysis: ⚡ Recommend Double (Expected ROI: 18.0%) 🔴 Double Risk Level: High (Kelly fraction: 0.180) ──────────────────────────────────────── 

• 🧪 Complete unit test coverage
• 📈 Add historical statistics feature
• 🎮 Implement split functionality
• 🔄 Add surrender option

• 🌐 Develop web interface version
• 💾 Implement game data persistence
• 📱 Mobile adaptation
• 🤖 AI opponent mode

• 🏆 Multiplayer functionality
• 📊 Detailed statistical reports
• 🎯 Custom rule settings
• 🌍 Internationalization support

Issues and Pull Requests are welcome!

git clone https://github.com/yourusername/go-blackjack.git cd go-blackjack go mod tidy go test ./...

• Follow Go official coding standards
• Use meaningful variable and function names
• Add appropriate comments and documentation

This project is licensed under the MIT License - see the LICENSE file for details

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