foxhunt/ml/examples/train_ppo.rs

//! PPO Training Example with Real DataBento Market Data
//!
//! Trains a PPO model on real market data from DBN files with:
//! - Real OHLCV data + technical indicators
//! - Actual PnL-based rewards
//! - GAE advantages on real price trajectories
//! - Policy convergence validation (KL divergence > 0)
//!
//! # Usage
//!
//! ```bash
//! # Train with default parameters (20 epochs)
//! cargo run -p ml --example train_ppo --release --features cuda
//!
//! # Custom epochs and output path
//! cargo run -p ml --example train_ppo --release --features cuda -- \
//!   --epochs 50 \
//!   --output-dir ml/trained_models \
//!   --data-dir test_data/real/databento
//! ```

use anyhow::{Context, Result};
use std::path::PathBuf;
use structopt::StructOpt;
use tracing::{info, warn};
use tracing_subscriber::FmtSubscriber;

use ml::real_data_loader::RealDataLoader;
use ml::trainers::ppo::{PpoHyperparameters, PpoTrainer, PpoTrainingMetrics};

#[derive(Debug, StructOpt)]
#[structopt(name = "train_ppo", about = "Train PPO model on real market data")]
struct Opts {
    /// Number of training epochs (default: 20 for policy convergence)
    #[structopt(long, default_value = "20")]
    epochs: usize,

    /// Learning rate
    #[structopt(long, default_value = "0.0003")]
    learning_rate: f64,

    /// Batch size (max 230 for RTX 3050 Ti 4GB)
    #[structopt(long, default_value = "64")]
    batch_size: usize,

    /// Output directory for trained model
    #[structopt(long, default_value = "ml/trained_models")]
    output_dir: String,

    /// Data directory containing DBN files
    #[structopt(long, default_value = "test_data/real/databento")]
    data_dir: String,

    /// Symbol to train on (ZN.FUT has ~29K bars)
    #[structopt(long, default_value = "ZN.FUT")]
    symbol: String,

    /// Verbose logging
    #[structopt(short, long)]
    verbose: bool,

    /// Enable early stopping (recommended, use --no-early-stopping to disable)
    #[structopt(long)]
    early_stopping: bool,

    /// Disable early stopping
    #[structopt(long)]
    no_early_stopping: bool,

    /// Minimum value loss improvement percentage for plateau detection
    #[structopt(long, default_value = "2.0")]
    min_value_loss_improvement: f64,

    /// Minimum explained variance threshold
    #[structopt(long, default_value = "0.4")]
    min_explained_variance: f64,

    /// Plateau detection window size (epochs)
    #[structopt(long, default_value = "30")]
    plateau_window: usize,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Parse CLI options
    let opts = Opts::from_args();

    // Setup logging
    let level = if opts.verbose {
        tracing::Level::DEBUG
    } else {
        tracing::Level::INFO
    };

    let subscriber = FmtSubscriber::builder().with_max_level(level).finish();
    tracing::subscriber::set_global_default(subscriber)
        .context("Failed to set tracing subscriber")?;

    info!("🚀 Starting PPO Training with Real DataBento Data");
    info!("Configuration:");
    info!("  • Epochs: {}", opts.epochs);
    info!("  • Learning rate: {}", opts.learning_rate);
    info!("  • Batch size: {}", opts.batch_size);
    info!("  • GPU: CUDA MANDATORY (no CPU fallback)");
    info!("  • Output directory: {}", opts.output_dir);
    info!("  • Data directory: {}", opts.data_dir);
    info!("  • Symbol: {}", opts.symbol);

    // Determine early stopping (enabled by default, unless --no-early-stopping is specified)
    let early_stopping_enabled = !opts.no_early_stopping;
    info!("  • Early stopping: {}", if early_stopping_enabled { "enabled" } else { "disabled" });
    if early_stopping_enabled {
        info!("    - Min value loss improvement: {}%", opts.min_value_loss_improvement);
        info!("    - Min explained variance: {}", opts.min_explained_variance);
        info!("    - Plateau window: {} epochs", opts.plateau_window);
    }

    // Create output directory
    let output_path = PathBuf::from(&opts.output_dir);
    if !output_path.exists() {
        std::fs::create_dir_all(&output_path)
            .context("Failed to create output directory")?;
        info!("✅ Created output directory: {}", opts.output_dir);
    }

    // Load real market data from DBN files
    info!("\n📊 Loading real market data from DBN files...");
    let mut loader = RealDataLoader::new(&opts.data_dir);
    let bars = loader.load_symbol_data(&opts.symbol).await
        .context(format!("Failed to load data for symbol: {}", opts.symbol))?;

    info!("✅ Loaded {} OHLCV bars for {}", bars.len(), opts.symbol);

    // Extract features and indicators
    info!("\n🔧 Extracting features and technical indicators...");
    let features = loader.extract_features(&bars)
        .context("Failed to extract features")?;
    let indicators = loader.calculate_indicators(&bars)
        .context("Failed to calculate indicators")?;

    info!("✅ Feature extraction complete:");
    info!("  • OHLCV bars: {}", features.prices.len());
    info!("  • Returns: {}", features.returns.len());
    info!("  • Volume: {}", features.volume.len());
    info!("  • Indicators: 10 technical indicators");

    // Build PPO state vectors (OHLCV + indicators + returns)
    // State: [open, high, low, close, volume, rsi, macd, macd_signal, bb_upper, bb_middle,
    //         bb_lower, atr, ema_fast, ema_slow, volume_ma, log_return]
    info!("\n🏗️  Building PPO state vectors...");
    let state_dim = 16; // 5 (OHLCV) + 10 (indicators) + 1 (return)
    let mut market_data = Vec::with_capacity(bars.len());

    for i in 0..bars.len() {
        let mut state = Vec::with_capacity(state_dim);

        // OHLCV (normalized 0-1)
        state.extend_from_slice(&features.prices[i]);

        // Technical indicators (10 values)
        state.push(indicators.rsi[i]);
        state.push(indicators.macd[i]);
        state.push(indicators.macd_signal[i]);
        state.push(indicators.bb_upper[i]);
        state.push(indicators.bb_middle[i]);
        state.push(indicators.bb_lower[i]);
        state.push(indicators.atr[i]);
        state.push(indicators.ema_fast[i]);
        state.push(indicators.ema_slow[i]);
        state.push(indicators.volume_ma[i]);

        // Log return
        state.push(features.returns[i]);

        market_data.push(state);
    }

    info!("✅ Built {} state vectors (dim={})", market_data.len(), state_dim);

    // Validate state dimensions
    if let Some(first_state) = market_data.first() {
        if first_state.len() != state_dim {
            return Err(anyhow::anyhow!(
                "State dimension mismatch: expected {}, got {}",
                state_dim,
                first_state.len()
            ));
        }
    }

    // Configure PPO hyperparameters
    let hyperparams = PpoHyperparameters {
        learning_rate: opts.learning_rate,
        batch_size: opts.batch_size,
        gamma: 0.99,
        clip_epsilon: 0.2,
        vf_coef: 0.5,
        ent_coef: 0.01,
        gae_lambda: 0.95,
        rollout_steps: 2048,
        minibatch_size: opts.batch_size,
        epochs: opts.epochs,
        early_stopping_enabled,
        min_value_loss_improvement_pct: opts.min_value_loss_improvement,
        min_explained_variance: opts.min_explained_variance,
        plateau_window: opts.plateau_window,
        min_epochs_before_stopping: 50,
    };

    // Create PPO trainer with real data state dimension
    let trainer = PpoTrainer::new(
        hyperparams.clone(),
        state_dim,
        &opts.output_dir,
        true,  // CUDA always required
    ).context("Failed to create PPO trainer")?;

    info!("✅ PPO trainer initialized (state_dim={})", state_dim);

    // Create progress callback with convergence tracking
    let mut policy_updates = 0;
    let mut kl_divergence_history = Vec::new();

    let progress_callback = |metrics: PpoTrainingMetrics| {
        // Track policy updates (KL divergence > 0 indicates policy changed)
        if metrics.kl_divergence > 0.0 {
            policy_updates += 1;
        }
        kl_divergence_history.push(metrics.kl_divergence);

        info!(
            "📊 Epoch {}/{}: policy_loss={:.4}, value_loss={:.4}, kl_div={:.6}, expl_var={:.4}, mean_reward={:.4}",
            metrics.epoch,
            hyperparams.epochs,
            metrics.policy_loss,
            metrics.value_loss,
            metrics.kl_divergence,
            metrics.explained_variance,
            metrics.mean_reward
        );
    };

    // Train the model
    info!("\n🏋️  Starting training...\n");
    let start_time = std::time::Instant::now();

    let final_metrics = trainer
        .train(market_data, progress_callback)
        .await
        .context("Training failed")?;

    let training_duration = start_time.elapsed();

    // Print final metrics
    info!("\n✅ Training completed successfully!");
    info!("\n📊 Final Metrics:");
    info!("  • Policy loss: {:.6}", final_metrics.policy_loss);
    info!("  • Value loss: {:.6}", final_metrics.value_loss);
    info!("  • KL divergence: {:.6}", final_metrics.kl_divergence);
    info!("  • Explained variance: {:.4}", final_metrics.explained_variance);
    info!("  • Mean reward: {:.4}", final_metrics.mean_reward);
    info!("  • Std reward: {:.4}", final_metrics.std_reward);
    info!("  • Entropy: {:.4}", final_metrics.entropy);
    info!("  • Training time: {:.1}s ({:.1} min)",
          training_duration.as_secs_f64(),
          training_duration.as_secs_f64() / 60.0);

    // Validate policy convergence
    info!("\n🔍 Policy Convergence Analysis:");
    info!("  • Total epochs: {}", hyperparams.epochs);
    info!("  • Policy updates (KL > 0): {}", policy_updates);
    info!("  • Policy update rate: {:.1}%",
          (policy_updates as f64 / hyperparams.epochs as f64) * 100.0);

    // Calculate KL divergence statistics
    let kl_mean = kl_divergence_history.iter().sum::<f32>() / kl_divergence_history.len() as f32;
    let kl_max = kl_divergence_history.iter().copied().fold(f32::NEG_INFINITY, f32::max);
    let kl_min = kl_divergence_history.iter().copied().fold(f32::INFINITY, f32::min);

    info!("  • KL divergence (mean): {:.6}", kl_mean);
    info!("  • KL divergence (max): {:.6}", kl_max);
    info!("  • KL divergence (min): {:.6}", kl_min);

    // Convergence validation
    if final_metrics.kl_divergence > 0.0 {
        info!("  ✅ PASS: Policy updates detected (KL divergence > 0)");
    } else {
        warn!("  ⚠️  WARN: No policy updates in final epoch (KL divergence = 0)");
        warn!("          This may indicate learning rate too low or convergence");
    }

    // Value function validation
    if final_metrics.explained_variance > 0.5 {
        info!("  ✅ PASS: Value network learning (explained variance > 0.5)");
    } else {
        warn!("  ⚠️  WARN: Value network may need tuning (explained variance < 0.5)");
    }

    // Checkpoint is already saved by trainer (every 10 epochs)
    let final_checkpoint = output_path.join(format!("ppo_checkpoint_epoch_{}.safetensors", hyperparams.epochs));

    info!("\n💾 Final checkpoint saved to: {}", final_checkpoint.display());
    info!("\n🎉 PPO training complete with real DataBento data!");
    info!("📁 Model files saved to: {}", opts.output_dir);

    info!("\n📈 Training Summary:");
    info!("  • Data source: Real DataBento OHLCV ({})", opts.symbol);
    info!("  • Training samples: {}", bars.len());
    info!("  • State dimension: {}", state_dim);
    info!("  • Features: OHLCV + 10 technical indicators + log returns");
    info!("  • Policy updates: {}/{} epochs ({:.1}%)",
          policy_updates,
          hyperparams.epochs,
          (policy_updates as f64 / hyperparams.epochs as f64) * 100.0);
    info!("  • Convergence: {}",
          if final_metrics.kl_divergence > 0.0 { "✅ Achieved" } else { "⚠️  Check logs" });

    Ok(())
}