Documentation

// OINK FM System Architecture
use solana_program::{
    account_info::AccountInfo,
    entrypoint,
    entrypoint::ProgramResult,
    msg,
    pubkey::Pubkey,
};

#[derive(Debug)]
pub struct OinkFMStation {
    pub authority: Pubkey,
    pub token_mint: Pubkey,
    pub burn_wallet: Pubkey,
    pub total_burned: u64,
    pub broadcast_hours: u64,
    pub ai_learning_state: AIState,
}

#[derive(Debug)]
pub struct AIState {
    pub porky_knowledge_base: Vec<KnowledgeNode>,
    pub perky_analysis_engine: AnalysisEngine,
    pub market_sentiment: f64,
    pub learning_iterations: u64,
}

# Add to your Cargo.toml
[dependencies]
oinkfm-sdk = "0.1.0"
solana-client = "1.17"
tokio = { version = "1.0", features = ["full"] }
anchor-client = "0.29.0"

use oinkfm_sdk::{OinkFMClient, SubmissionType};
use solana_client::RpcClient;
use std::env;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize OINK FM client
    let rpc_url = env::var("SOLANA_RPC_URL")
        .unwrap_or_else(|_| "https://api.mainnet-beta.solana.com".to_string());
    
    let client = OinkFMClient::new(&rpc_url).await?;
    
    // Get current station stats
    let stats = client.get_station_stats().await?;
    println!("Total tokens burned: {}", stats.total_burned);
    println!("Hours broadcasted: {}", stats.broadcast_hours);
    
    // Submit content to OINK FM
    let submission = client
        .submit_content(
            SubmissionType::Message {
                content: "Hello from Rust!".to_string(),
                author: "RustDev".to_string(),
            },
            500, // 500 OINKFM tokens
        )
        .await?;
    
    println!("Submission ID: {}", submission.id);
    
    Ok(())
}

use anchor_client::{
    solana_sdk::{
        commitment_config::CommitmentConfig,
        pubkey::Pubkey,
        signature::Signature,
        signer::Signer,
    },
    Client, Cluster,
};
use serde::{Deserialize, Serialize};
use std::rc::Rc;

#[derive(Debug, Clone)]
pub struct OinkFMClient {
    pub client: Client,
    pub program_id: Pubkey,
    pub station_account: Pubkey,
}

#[derive(Debug, Serialize, Deserialize)]
pub enum SubmissionType {
    Video {
        project_name: String,
        video_url: String,
        duration_seconds: u32,
    },
    Song {
        title: String,
        artist: String,
        requester: Option<String>,
    },
    Message {
        content: String,
        author: String,
        category: MessageCategory,
    },
}

#[derive(Debug, Serialize, Deserialize)]
pub enum MessageCategory {
    MarketAnalysis,
    Story,
    Shoutout,
    Question,
    Other,
}

impl OinkFMClient {
    /// Create a new OINK FM client
    pub async fn new(rpc_url: &str) -> Result<Self, ClientError> {
        let cluster = Cluster::Custom(rpc_url.to_string(), rpc_url.to_string());
        let client = Client::new_with_options(
            cluster,
            Rc::new(Keypair::new()), // Dummy keypair for read operations
            CommitmentConfig::processed(),
        );

        let program_id = "OINK_PROGRAM_ID_HERE".parse()?;
        let station_account = "STATION_ACCOUNT_HERE".parse()?;

        Ok(Self {
            client,
            program_id,
            station_account,
        })
    }

    /// Get current station statistics
    pub async fn get_station_stats(&self) -> Result<StationStats, ClientError> {
        let account_data = self
            .client
            .rpc()
            .get_account_data(&self.station_account)
            .await?;

        let station: OinkFMStation = OinkFMStation::try_deserialize(&mut account_data.as_slice())?;

        Ok(StationStats {
            total_burned: station.total_burned,
            broadcast_hours: station.broadcast_hours,
            current_listeners: self.get_listener_count().await?,
            ai_iterations: station.ai_learning_state.learning_iterations,
        })
    }

    /// Submit content to OINK FM
    pub async fn submit_content(
        &self,
        submission: SubmissionType,
        token_amount: u64,
        payer: &dyn Signer,
    ) -> Result<SubmissionResult, ClientError> {
        let cost = match &submission {
            SubmissionType::Video { .. } => 10_000,
            SubmissionType::Song { .. } => 100,
            SubmissionType::Message { .. } => 500,
        };

        if token_amount != cost {
            return Err(ClientError::InvalidTokenAmount { 
                expected: cost, 
                provided: token_amount 
            });
        }

        // Execute burn transaction
        let burn_signature = self.burn_tokens(payer, token_amount).await?;
        
        // Submit content metadata
        let submission_id = self.create_submission_record(submission, burn_signature).await?;

        Ok(SubmissionResult {
            id: submission_id,
            burn_signature,
            status: SubmissionStatus::PendingReview,
        })
    }

    /// Get AI sentiment analysis
    pub async fn get_ai_sentiment(&self) -> Result<AISentiment, ClientError> {
        let response = self
            .client
            .request_airdrop(&self.station_account, 0) // Dummy call to test connection
            .await;

        // In a real implementation, this would call the AI sentiment endpoint
        // For now, we'll return mock data
        Ok(AISentiment {
            overall_sentiment: 0.67,
            porky_sentiment: 0.72,
            perky_sentiment: 0.62,
            confidence: 0.89,
            last_updated: chrono::Utc::now(),
        })
    }

    /// Private helper methods
    async fn burn_tokens(
        &self,
        payer: &dyn Signer,
        amount: u64,
    ) -> Result<Signature, ClientError> {
        // Implementation for burning tokens
        // This would use the Solana token program to burn tokens
        todo!("Implement token burn logic")
    }

    async fn create_submission_record(
        &self,
        submission: SubmissionType,
        burn_sig: Signature,
    ) -> Result<String, ClientError> {
        // Implementation for creating submission record
        todo!("Implement submission record creation")
    }

    async fn get_listener_count(&self) -> Result<u32, ClientError> {
        // Mock implementation
        Ok(1337)
    }
}

#[derive(Debug, Serialize, Deserialize)]
pub struct StationStats {
    pub total_burned: u64,
    pub broadcast_hours: u64,
    pub current_listeners: u32,
    pub ai_iterations: u64,
}

#[derive(Debug, Serialize, Deserialize)]
pub struct SubmissionResult {
    pub id: String,
    pub burn_signature: Signature,
    pub status: SubmissionStatus,
}

#[derive(Debug, Serialize, Deserialize)]
pub enum SubmissionStatus {
    PendingReview,
    Approved,
    Scheduled,
    Broadcasted,
    Rejected,
}

#[derive(Debug, Serialize, Deserialize)]
pub struct AISentiment {
    pub overall_sentiment: f64, // -1.0 to 1.0
    pub porky_sentiment: f64,
    pub perky_sentiment: f64,
    pub confidence: f64,
    pub last_updated: chrono::DateTime<chrono::Utc>,
}

#[derive(Debug)]
pub enum ClientError {
    NetworkError(String),
    InvalidTokenAmount { expected: u64, provided: u64 },
    SerializationError(String),
    InvalidSignature,
    InsufficientFunds,
    RateLimitExceeded,
}

impl std::fmt::Display for ClientError {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        match self {
            ClientError::NetworkError(msg) => write!(f, "Network error: {}", msg),
            ClientError::InvalidTokenAmount { expected, provided } => {
                write!(f, "Invalid token amount: expected {}, got {}", expected, provided)
            }
            ClientError::SerializationError(msg) => write!(f, "Serialization error: {}", msg),
            ClientError::InvalidSignature => write!(f, "Invalid signature"),
            ClientError::InsufficientFunds => write!(f, "Insufficient funds"),
            ClientError::RateLimitExceeded => write!(f, "Rate limit exceeded"),
        }
    }
}

impl std::error::Error for ClientError {}

use anchor_lang::prelude::*;
use anchor_spl::{
    token::{Mint, Token, TokenAccount, Burn, burn},
};

#[program]
pub mod oinkfm_program {
    use super::*;

    /// Burn tokens for content submission
    pub fn burn_for_submission(
        ctx: Context<BurnForSubmission>,
        amount: u64,
        submission_type: SubmissionType,
    ) -> Result<()> {
        let required_amount = match submission_type {
            SubmissionType::Video => 10_000 * 10_u64.pow(9), // 10,000 OINKFM
            SubmissionType::Song => 100 * 10_u64.pow(9),     // 100 OINKFM
            SubmissionType::Message => 500 * 10_u64.pow(9),   // 500 OINKFM
        };

        require!(amount == required_amount, OinkFMError::InvalidBurnAmount);

        // Burn tokens
        let burn_ctx = CpiContext::new(
            ctx.accounts.token_program.to_account_info(),
            Burn {
                mint: ctx.accounts.mint.to_account_info(),
                from: ctx.accounts.user_token_account.to_account_info(),
                authority: ctx.accounts.user.to_account_info(),
            },
        );

        burn(burn_ctx, amount)?;

        // Update station stats
        let station = &mut ctx.accounts.station;
        station.total_burned = station.total_burned.checked_add(amount)
            .ok_or(OinkFMError::MathOverflow)?;
        
        station.total_submissions = station.total_submissions.checked_add(1)
            .ok_or(OinkFMError::MathOverflow)?;

        // Emit burn event
        emit!(TokenBurnEvent {
            amount,
            submission_type,
            user: ctx.accounts.user.key(),
            timestamp: Clock::get()?.unix_timestamp,
        });

        Ok(())
    }

    /// Calculate dynamic pricing based on demand
    pub fn calculate_dynamic_pricing(
        base_price: u64,
        recent_submissions: u32,
        time_window_hours: u32,
    ) -> u64 {
        let submissions_per_hour = recent_submissions as f64 / time_window_hours as f64;
        
        // Apply demand multiplier (exponential scaling)
        let demand_multiplier = if submissions_per_hour > 10.0 {
            1.0 + ((submissions_per_hour - 10.0) / 10.0).min(2.0)
        } else {
            1.0
        };

        (base_price as f64 * demand_multiplier) as u64
    }

    /// Update AI learning state with market data
    pub fn update_ai_learning(
        ctx: Context<UpdateAILearning>,
        market_data: MarketData,
    ) -> Result<()> {
        let station = &mut ctx.accounts.station;
        
        // Update Porky's analysis engine
        station.ai_learning_state.porky_sentiment = calculate_sentiment(
            &market_data,
            AIPersonality::Porky,
        );

        // Update Perky's analysis engine
        station.ai_learning_state.perky_sentiment = calculate_sentiment(
            &market_data,
            AIPersonality::Perky,
        );

        // Increment learning iterations
        station.ai_learning_state.learning_iterations = 
            station.ai_learning_state.learning_iterations.checked_add(1)
                .ok_or(OinkFMError::MathOverflow)?;

        // Store market data in knowledge base
        station.ai_learning_state.knowledge_base.push(KnowledgeNode {
            timestamp: Clock::get()?.unix_timestamp,
            data_type: DataType::MarketData,
            content: market_data.serialize()?,
            importance_score: calculate_importance(&market_data),
        });

        Ok(())
    }
}

#[derive(Accounts)]
pub struct BurnForSubmission<'info> {
    #[account(mut)]
    pub user: Signer<'info>,
    
    #[account(mut)]
    pub user_token_account: Account<'info, TokenAccount>,
    
    #[account(mut)]
    pub mint: Account<'info, Mint>,
    
    #[account(mut)]
    pub station: Account<'info, OinkFMStation>,
    
    pub token_program: Program<'info, Token>,
}

#[derive(AnchorSerialize, AnchorDeserialize, Clone)]
pub enum SubmissionType {
    Video,
    Song,
    Message,
}

#[event]
pub struct TokenBurnEvent {
    pub amount: u64,
    pub submission_type: SubmissionType,
    pub user: Pubkey,
    pub timestamp: i64,
}

#[error_code]
pub enum OinkFMError {
    #[msg("Invalid burn amount for submission type")]
    InvalidBurnAmount,
    #[msg("Mathematical overflow occurred")]
    MathOverflow,
    #[msg("AI learning state corrupted")]
    AIStateCorrupted,
}

#[derive(Debug, Clone)]
pub struct TokenomicsCalculator {
    pub total_supply: u64,
    pub distribution: TokenDistribution,
}

#[derive(Debug, Clone)]
pub struct TokenDistribution {
    pub public_sale: u64,      // 50%
    pub holder_rewards: u64,   // 20.5%
    pub creator_rewards: u64,  // 12.5%
    pub liquidity_pool: u64,   // 10%
    pub team_allocation: u64,  // 7%
}

impl TokenomicsCalculator {
    pub fn new(total_supply: u64) -> Self {
        let distribution = TokenDistribution {
            public_sale: (total_supply as f64 * 0.50) as u64,
            holder_rewards: (total_supply as f64 * 0.205) as u64,
            creator_rewards: (total_supply as f64 * 0.125) as u64,
            liquidity_pool: (total_supply as f64 * 0.10) as u64,
            team_allocation: (total_supply as f64 * 0.07) as u64,
        };

        Self {
            total_supply,
            distribution,
        }
    }

    /// Calculate burn rate impact on token economics
    pub fn calculate_burn_impact(
        &self,
        burned_amount: u64,
        time_period_days: u32,
    ) -> BurnImpactAnalysis {
        let burn_rate_daily = burned_amount as f64 / time_period_days as f64;
        let annual_burn_rate = burn_rate_daily * 365.0;
        let burn_percentage = (burned_amount as f64 / self.total_supply as f64) * 100.0;
        
        let deflationary_pressure = if annual_burn_rate > 0.0 {
            (annual_burn_rate / self.total_supply as f64) * 100.0
        } else {
            0.0
        };

        BurnImpactAnalysis {
            burned_amount,
            burn_percentage,
            daily_burn_rate: burn_rate_daily,
            annual_burn_rate,
            deflationary_pressure,
            remaining_supply: self.total_supply - burned_amount,
        }
    }

    /// Project future supply given current burn rate
    pub fn project_future_supply(
        &self,
        current_burned: u64,
        daily_burn_rate: f64,
        days_ahead: u32,
    ) -> SupplyProjection {
        let future_burned = current_burned + (daily_burn_rate * days_ahead as f64) as u64;
        let future_supply = self.total_supply.saturating_sub(future_burned);
        
        SupplyProjection {
            days_ahead,
            projected_burned: future_burned,
            projected_supply: future_supply,
            scarcity_multiplier: self.total_supply as f64 / future_supply as f64,
        }
    }
}

#[derive(Debug)]
pub struct BurnImpactAnalysis {
    pub burned_amount: u64,
    pub burn_percentage: f64,
    pub daily_burn_rate: f64,
    pub annual_burn_rate: f64,
    pub deflationary_pressure: f64,
    pub remaining_supply: u64,
}

#[derive(Debug)]
pub struct SupplyProjection {
    pub days_ahead: u32,
    pub projected_burned: u64,
    pub projected_supply: u64,
    pub scarcity_multiplier: f64,
}

use serde::{Deserialize, Serialize};
use std::collections::HashMap;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AIPersonalityEngine {
    pub porky: AIPersonality,
    pub perky: AIPersonality,
    pub knowledge_base: SharedKnowledgeBase,
    pub interaction_history: Vec<Interaction>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AIPersonality {
    pub name: String,
    pub personality_traits: PersonalityTraits,
    pub analysis_engine: AnalysisEngine,
    pub learning_parameters: LearningParameters,
    pub sentiment_bias: f64,
    pub confidence_threshold: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PersonalityTraits {
    pub skepticism: f64,       // 0.0-1.0: How skeptical of claims
    pub optimism: f64,         // 0.0-1.0: Market outlook bias
    pub aggression: f64,       // 0.0-1.0: How forceful in opinions
    pub conspiracy_tendency: f64, // 0.0-1.0: Tendency to see manipulation
    pub technical_focus: f64,  // 0.0-1.0: TA vs fundamental analysis
}

impl AIPersonalityEngine {
    pub fn new() -> Self {
        let porky = AIPersonality {
            name: "Porky".to_string(),
            personality_traits: PersonalityTraits {
                skepticism: 0.8,           // Highly skeptical
                optimism: 0.3,             // Generally pessimistic
                aggression: 0.9,           // Very forceful
                conspiracy_tendency: 0.9,  // High conspiracy detection
                technical_focus: 0.4,      // More fundamental analysis
            },
            analysis_engine: AnalysisEngine::new_porky(),
            learning_parameters: LearningParameters::conservative(),
            sentiment_bias: -0.2, // Slightly bearish bias
            confidence_threshold: 0.7,
        };

        let perky = AIPersonality {
            name: "Perky".to_string(),
            personality_traits: PersonalityTraits {
                skepticism: 0.6,           // Moderately skeptical
                optimism: 0.7,             // Generally optimistic
                aggression: 0.5,           // Balanced approach
                conspiracy_tendency: 0.7,  // Moderate conspiracy detection
                technical_focus: 0.8,      // Heavy technical analysis
            },
            analysis_engine: AnalysisEngine::new_perky(),
            learning_parameters: LearningParameters::aggressive(),
            sentiment_bias: 0.1, // Slightly bullish bias
            confidence_threshold: 0.6,
        };

        Self {
            porky,
            perky,
            knowledge_base: SharedKnowledgeBase::new(),
            interaction_history: Vec::new(),
        }
    }

    /// Process market data and generate analysis from both personalities
    pub fn analyze_market_data(
        &mut self,
        market_data: &MarketData,
    ) -> DualAnalysis {
        // Porky's analysis (skeptical, conspiracy-focused)
        let porky_analysis = self.porky.analyze(market_data, &self.knowledge_base);
        
        // Perky's analysis (technical, optimistic)
        let perky_analysis = self.perky.analyze(market_data, &self.knowledge_base);

        // Update shared knowledge base
        self.knowledge_base.add_market_data(market_data);

        // Generate combined insight
        let consensus = self.find_consensus(&porky_analysis, &perky_analysis);

        DualAnalysis {
            porky: porky_analysis,
            perky: perky_analysis,
            consensus,
            timestamp: chrono::Utc::now(),
        }
    }

    /// Generate dialogue between personalities
    pub fn generate_dialogue(
        &mut self,
        topic: &DialogueTopic,
    ) -> Vec<DialogueExchange> {
        let mut dialogue = Vec::new();
        
        // Porky opens with skeptical take
        let porky_opening = self.porky.generate_statement(topic, &self.knowledge_base);
        dialogue.push(DialogueExchange {
            speaker: "Porky".to_string(),
            content: porky_opening,
            sentiment: self.porky.calculate_sentiment(topic),
            timestamp: chrono::Utc::now(),
        });

        // Perky responds with technical perspective
        let perky_response = self.perky.respond_to(&dialogue[0], topic, &self.knowledge_base);
        dialogue.push(DialogueExchange {
            speaker: "Perky".to_string(),
            content: perky_response,
            sentiment: self.perky.calculate_sentiment(topic),
            timestamp: chrono::Utc::now(),
        });

        // Continue dialogue for 3-5 exchanges
        for i in 0..3 {
            if i % 2 == 0 {
                // Porky's turn
                let response = self.porky.continue_dialogue(&dialogue, topic);
                dialogue.push(DialogueExchange {
                    speaker: "Porky".to_string(),
                    content: response,
                    sentiment: self.porky.calculate_sentiment(topic),
                    timestamp: chrono::Utc::now(),
                });
            } else {
                // Perky's turn
                let response = self.perky.continue_dialogue(&dialogue, topic);
                dialogue.push(DialogueExchange {
                    speaker: "Perky".to_string(),
                    content: response,
                    sentiment: self.perky.calculate_sentiment(topic),
                    timestamp: chrono::Utc::now(),
                });
            }
        }

        // Store dialogue in interaction history
        self.interaction_history.push(Interaction {
            interaction_type: InteractionType::Dialogue,
            topic: topic.clone(),
            exchanges: dialogue.clone(),
            timestamp: chrono::Utc::now(),
        });

        dialogue
    }

    /// Learn from user feedback and market outcomes
    pub fn learn_from_feedback(
        &mut self,
        feedback: &UserFeedback,
        market_outcome: &MarketOutcome,
    ) {
        // Update Porky's learning parameters based on accuracy
        if feedback.porky_accuracy > 0.7 {
            self.porky.learning_parameters.learning_rate *= 1.1;
        } else {
            self.porky.learning_parameters.learning_rate *= 0.9;
        }

        // Update Perky's learning parameters
        if feedback.perky_accuracy > 0.7 {
            self.perky.learning_parameters.learning_rate *= 1.1;
        } else {
            self.perky.learning_parameters.learning_rate *= 0.9;
        }

        // Update personality traits based on market outcomes
        self.adjust_personality_traits(market_outcome);
        
        // Add to knowledge base
        self.knowledge_base.add_feedback(feedback, market_outcome);
    }

    fn find_consensus(
        &self,
        porky: &Analysis,
        perky: &Analysis,
    ) -> ConsensusAnalysis {
        let avg_sentiment = (porky.sentiment + perky.sentiment) / 2.0;
        let confidence_spread = (porky.confidence - perky.confidence).abs();
        
        let agreement_level = if confidence_spread < 0.2 {
            AgreementLevel::HighAgreement
        } else if confidence_spread < 0.5 {
            AgreementLevel::ModerateAgreement
        } else {
            AgreementLevel::Disagreement
        };

        ConsensusAnalysis {
            combined_sentiment: avg_sentiment,
            agreement_level,
            key_points: self.extract_common_points(porky, perky),
            divergent_views: self.extract_divergent_views(porky, perky),
        }
    }

    fn adjust_personality_traits(&mut self, outcome: &MarketOutcome) {
        // If market crashed and Porky was more bearish, increase his confidence
        if outcome.market_change < -0.1 && self.porky.sentiment_bias < 0.0 {
            self.porky.personality_traits.skepticism = 
                (self.porky.personality_traits.skepticism + 0.05).min(1.0);
        }

        // If market pumped and Perky was bullish, increase his optimism
        if outcome.market_change > 0.1 && self.perky.sentiment_bias > 0.0 {
            self.perky.personality_traits.optimism = 
                (self.perky.personality_traits.optimism + 0.05).min(1.0);
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LearningParameters {
    pub learning_rate: f64,
    pub memory_decay: f64,
    pub pattern_recognition_threshold: f64,
    pub adaptation_speed: f64,
}

impl LearningParameters {
    pub fn conservative() -> Self {
        Self {
            learning_rate: 0.01,
            memory_decay: 0.995,
            pattern_recognition_threshold: 0.8,
            adaptation_speed: 0.5,
        }
    }

    pub fn aggressive() -> Self {
        Self {
            learning_rate: 0.05,
            memory_decay: 0.99,
            pattern_recognition_threshold: 0.6,
            adaptation_speed: 1.2,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SharedKnowledgeBase {
    pub market_patterns: HashMap<String, PatternData>,
    pub token_histories: HashMap<String, TokenHistory>,
    pub sentiment_correlations: Vec<SentimentCorrelation>,
    pub manipulation_indicators: Vec<ManipulationPattern>,
    pub learning_iterations: u64,
}

impl SharedKnowledgeBase {
    pub fn new() -> Self {
        Self {
            market_patterns: HashMap::new(),
            token_histories: HashMap::new(),
            sentiment_correlations: Vec::new(),
            manipulation_indicators: Vec::new(),
            learning_iterations: 0,
        }
    }

    pub fn add_market_data(&mut self, data: &MarketData) {
        // Update token history
        let history = self
            .token_histories
            .entry(data.symbol.clone())
            .or_insert_with(TokenHistory::new);
        
        history.add_data_point(data);

        // Detect patterns
        if let Some(pattern) = self.detect_pattern(data, history) {
            self.market_patterns.insert(pattern.id.clone(), pattern);
        }

        // Check for manipulation signals
        if let Some(manipulation) = self.detect_manipulation(data, history) {
            self.manipulation_indicators.push(manipulation);
        }

        self.learning_iterations += 1;
    }

    fn detect_pattern(
        &self,
        current: &MarketData,
        history: &TokenHistory,
    ) -> Option<PatternData> {
        // Implement pattern detection algorithms
        // Look for common crypto patterns: pump & dump, accumulation, etc.
        
        if history.data_points.len() < 10 {
            return None;
        }

        let recent_volume = history.recent_average_volume(5);
        let historical_volume = history.historical_average_volume();

        // Detect pump pattern
        if current.volume > historical_volume * 5.0 
            && current.price_change > 0.2 {
            return Some(PatternData {
                id: format!("pump_{}", current.timestamp),
                pattern_type: PatternType::Pump,
                confidence: 0.8,
                timestamp: current.timestamp,
                metadata: format!("Volume spike: {}x normal", 
                    current.volume / historical_volume),
            });
        }

        None
    }

    fn detect_manipulation(
        &self,
        current: &MarketData,
        history: &TokenHistory,
    ) -> Option<ManipulationPattern> {
        // Porky's specialty: detecting market manipulation
        
        // Look for wash trading patterns
        if self.is_wash_trading(current, history) {
            return Some(ManipulationPattern {
                pattern_type: ManipulationType::WashTrading,
                confidence: 0.75,
                evidence: vec![
                    "Repetitive volume patterns".to_string(),
                    "Unusual bid-ask spreads".to_string(),
                ],
                timestamp: current.timestamp,
            });
        }

        // Look for coordinated pump signals
        if self.is_coordinated_pump(current, history) {
            return Some(ManipulationPattern {
                pattern_type: ManipulationType::CoordinatedPump,
                confidence: 0.85,
                evidence: vec![
                    "Sudden volume spike".to_string(),
                    "Social media coordination signals".to_string(),
                ],
                timestamp: current.timestamp,
            });
        }

        None
    }

    fn is_wash_trading(&self, current: &MarketData, history: &TokenHistory) -> bool {
        // Simplified wash trading detection
        // In reality, this would be much more sophisticated
        let volume_variance = history.volume_variance();
        let price_volatility = history.price_volatility();
        
        // High volume with low price movement suggests wash trading
        current.volume > history.average_volume() * 3.0 
            && current.price_change.abs() < 0.02
            && volume_variance > 2.0
    }

    fn is_coordinated_pump(&self, current: &MarketData, history: &TokenHistory) -> bool {
        // Look for pump characteristics
        let volume_spike = current.volume / history.average_volume();
        let price_spike = current.price_change;
        
        volume_spike > 10.0 && price_spike > 0.5 && current.market_cap < 10_000_000.0
    }
}

#[derive(Debug, Clone)]
pub struct ResponseGenerator {
    pub porky_templates: Vec<ResponseTemplate>,
    pub perky_templates: Vec<ResponseTemplate>,
    pub context_window: usize,
}

#[derive(Debug, Clone)]
pub struct ResponseTemplate {
    pub trigger_conditions: Vec<TriggerCondition>,
    pub response_patterns: Vec<String>,
    pub personality_weight: f64,
    pub context_sensitivity: f64,
}

impl ResponseGenerator {
    pub fn new() -> Self {
        let porky_templates = vec![
            ResponseTemplate {
                trigger_conditions: vec![
                    TriggerCondition::MarketPump,
                    TriggerCondition::HighVolume,
                ],
                response_patterns: vec![
                    "Hold up, folks! This pump looks suspicious...".to_string(),
                    "I'm seeing all the classic signs of manipulation here...".to_string(),
                    "Don't fall for this obvious pump and dump scheme!".to_string(),
                ],
                personality_weight: 0.9, // High weight for Porky's skepticism
                context_sensitivity: 0.8,
            },
            ResponseTemplate {
                trigger_conditions: vec![
                    TriggerCondition::MarketCrash,
                    TriggerCondition::HighFear,
                ],
                response_patterns: vec![
                    "I told you this was coming! The whales are dumping...".to_string(),
                    "This crash was orchestrated. Follow the whale wallets!".to_string(),
                    "Retail gets rekt again while insiders profit...".to_string(),
                ],
                personality_weight: 0.95,
                context_sensitivity: 0.7,
            },
        ];

        let perky_templates = vec![
            ResponseTemplate {
                trigger_conditions: vec![
                    TriggerCondition::TechnicalBreakout,
                    TriggerCondition::VolumeConfirmation,
                ],
                response_patterns: vec![
                    "Now this is a clean breakout! RSI confirming...".to_string(),
                    "The technicals are screaming bullish divergence!".to_string(),
                    "Volume profile supports this move higher...".to_string(),
                ],
                personality_weight: 0.85,
                context_sensitivity: 0.9,
            },
            ResponseTemplate {
                trigger_conditions: vec![
                    TriggerCondition::BearishDivergence,
                    TriggerCondition::WeakVolume,
                ],
                response_patterns: vec![
                    "Hmm, seeing bearish divergence on the indicators...".to_string(),
                    "Volume isn't confirming this move. Could be a fake-out...".to_string(),
                    "The charts are warning us - momentum is weakening...".to_string(),
                ],
                personality_weight: 0.7,
                context_sensitivity: 0.85,
            },
        ];

        Self {
            porky_templates,
            perky_templates,
            context_window: 10,
        }
    }

    pub fn generate_porky_response(
        &self,
        market_data: &MarketData,
        context: &[DialogueExchange],
        knowledge_base: &SharedKnowledgeBase,
    ) -> String {
        // Find matching templates based on current conditions
        let conditions = self.analyze_conditions(market_data, knowledge_base);
        let matching_templates = self.find_matching_templates(&self.porky_templates, &conditions);

        if matching_templates.is_empty() {
            return self.generate_fallback_porky_response(market_data);
        }

        // Select best template based on context and personality weight
        let best_template = self.select_best_template(&matching_templates, context);
        
        // Generate response with dynamic content injection
        self.inject_dynamic_content(
            &best_template.response_patterns[0], 
            market_data, 
            knowledge_base
        )
    }

    pub fn generate_perky_response(
        &self,
        market_data: &MarketData,
        context: &[DialogueExchange],
        knowledge_base: &SharedKnowledgeBase,
    ) -> String {
        let conditions = self.analyze_conditions(market_data, knowledge_base);
        let matching_templates = self.find_matching_templates(&self.perky_templates, &conditions);

        if matching_templates.is_empty() {
            return self.generate_fallback_perky_response(market_data);
        }

        let best_template = self.select_best_template(&matching_templates, context);
        
        self.inject_dynamic_content(
            &best_template.response_patterns[0], 
            market_data, 
            knowledge_base
        )
    }

    fn inject_dynamic_content(
        &self,
        template: &str,
        market_data: &MarketData,
        knowledge_base: &SharedKnowledgeBase,
    ) -> String {
        let mut response = template.to_string();

        // Inject real market data
        if market_data.price_change > 0.0 {
            response = response.replace("{PRICE_MOVEMENT}", 
                &format!("up {:.2}%", market_data.price_change * 100.0));
        } else {
            response = response.replace("{PRICE_MOVEMENT}", 
                &format!("down {:.2}%", market_data.price_change.abs() * 100.0));
        }

        // Inject volume data
        if let Some(token_history) = knowledge_base.token_histories.get(&market_data.symbol) {
            let volume_multiple = market_data.volume / token_history.average_volume();
            if volume_multiple > 2.0 {
                response = response.replace("{VOLUME_ANALYSIS}", 
                    &format!("Volume is {}x normal!", volume_multiple as u32));
            }
        }

        // Add timestamp context
        response = response.replace("{TOKEN}", &market_data.symbol);

        response
    }

    fn analyze_conditions(
        &self,
        market_data: &MarketData,
        knowledge_base: &SharedKnowledgeBase,
    ) -> Vec<TriggerCondition> {
        let mut conditions = Vec::new();

        // Analyze market conditions
        if market_data.price_change > 0.2 {
            conditions.push(TriggerCondition::MarketPump);
        } else if market_data.price_change < -0.15 {
            conditions.push(TriggerCondition::MarketCrash);
        }

        // Check volume conditions
        if let Some(history) = knowledge_base.token_histories.get(&market_data.symbol) {
            if market_data.volume > history.average_volume() * 3.0 {
                conditions.push(TriggerCondition::HighVolume);
            }
        }

        // Check for manipulation patterns
        if knowledge_base.manipulation_indicators
            .iter()
            .any(|m| m.timestamp > market_data.timestamp - 3600) {
            conditions.push(TriggerCondition::ManipulationDetected);
        }

        conditions
    }
}

// Supporting data structures
#[derive(Debug, Clone, PartialEq)]
pub enum TriggerCondition {
    MarketPump,
    MarketCrash,
    HighVolume,
    WeakVolume,
    TechnicalBreakout,
    BearishDivergence,
    VolumeConfirmation,
    HighFear,
    ManipulationDetected,
}

#[derive(Debug, Clone)]
pub struct DialogueExchange {
    pub speaker: String,
    pub content: String,
    pub sentiment: f64,
    pub timestamp: chrono::DateTime<chrono::Utc>,
}