7d9f1c6e17
Add ml/examples/train_baseline.rs that trains DQN and PPO models using expanding walk-forward windows on real Databento OHLCV data. Features: - CLI args via clap (--model, --epochs, --batch-size, --data-dir, etc.) - Recursive .dbn.zst file discovery and OHLCV bar loading - 51-dim feature extraction via extract_ml_features() - Walk-forward window generation with NormStats per fold - DQN training loop with epsilon-greedy, experience replay, early stopping - PPO training loop with GAE, trajectory collection, early stopping - PnL-based reward (BUY/SELL/HOLD) - Safetensors checkpoint saving per fold - NormStats JSON export for evaluation reproducibility Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
835 lines
28 KiB
Rust
835 lines
28 KiB
Rust
//! Walk-forward training binary for DQN and PPO models.
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//!
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//! Trains models using expanding walk-forward windows on real OHLCV data loaded
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//! from Databento DBN files. Supports early stopping, checkpoint saving, and
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//! normalization statistics export for reproducible evaluation.
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//!
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//! # Usage
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//!
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//! ```bash
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//! SQLX_OFFLINE=true cargo run -p ml --example train_baseline -- \
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//! --model both --epochs 50 --batch-size 128 \
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//! --data-dir data/cache/futures-baseline \
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//! --output-dir ml/trained_models
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//! ```
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#![allow(unused_crate_dependencies)]
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#![deny(
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clippy::unwrap_used,
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clippy::expect_used,
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clippy::panic,
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clippy::indexing_slicing
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)]
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use std::path::{Path, PathBuf};
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use anyhow::{Context, Result};
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use chrono::{DateTime, TimeZone, Utc};
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use clap::Parser;
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use rand::Rng;
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use tracing::{error, info, warn};
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use dbn::decode::DecodeRecord;
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use ml::dqn::{DQNConfig, Experience, DQN};
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use ml::features::extraction::extract_ml_features;
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use ml::ppo::ppo::{PPOConfig, PPO};
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use ml::ppo::trajectories::{Trajectory, TrajectoryBatch, TrajectoryStep};
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use ml::ppo::gae::compute_gae;
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use ml::types::OHLCVBar;
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use ml::walk_forward::{generate_walk_forward_windows, NormStats, WalkForwardConfig};
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// ---------------------------------------------------------------------------
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// CLI Arguments
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// ---------------------------------------------------------------------------
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/// Walk-forward training binary for DQN and PPO baseline models.
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#[derive(Parser, Debug)]
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#[command(name = "train_baseline", about = "Train DQN/PPO with walk-forward windows")]
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struct Args {
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/// Which model(s) to train: "dqn", "ppo", or "both"
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#[arg(long, default_value = "both")]
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model: String,
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/// Maximum training epochs per fold
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#[arg(long, default_value_t = 50)]
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epochs: usize,
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/// Training batch size
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#[arg(long, default_value_t = 128)]
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batch_size: usize,
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/// Path to directory containing .dbn.zst files
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#[arg(long, default_value = "data/cache/futures-baseline")]
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data_dir: PathBuf,
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/// Output directory for trained model checkpoints
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#[arg(long, default_value = "ml/trained_models")]
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output_dir: PathBuf,
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/// Optional path to hyperopt results JSON (reserved for future use)
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#[arg(long)]
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hyperopt_params: Option<PathBuf>,
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/// Feature dimension (must match extract_ml_features output)
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#[arg(long, default_value_t = 51)]
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feature_dim: usize,
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/// Early stopping patience (epochs without improvement)
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#[arg(long, default_value_t = 10)]
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patience: usize,
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/// Number of actions for the DQN/PPO action space
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#[arg(long, default_value_t = 3)]
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num_actions: usize,
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}
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// ---------------------------------------------------------------------------
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// DBN Loading
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// ---------------------------------------------------------------------------
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/// Recursively discover .dbn.zst files under `dir`.
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fn find_dbn_files(dir: &Path) -> Result<Vec<PathBuf>> {
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let mut files = Vec::new();
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if !dir.exists() {
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anyhow::bail!("Data directory does not exist: {}", dir.display());
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}
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collect_dbn_files_recursive(dir, &mut files)?;
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files.sort();
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Ok(files)
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}
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/// Recursive helper that walks the directory tree without `walkdir`.
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fn collect_dbn_files_recursive(dir: &Path, out: &mut Vec<PathBuf>) -> Result<()> {
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let entries = std::fs::read_dir(dir)
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.with_context(|| format!("Cannot read directory: {}", dir.display()))?;
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for entry in entries {
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let entry = entry.with_context(|| "Failed to read dir entry")?;
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let path = entry.path();
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if path.is_dir() {
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collect_dbn_files_recursive(&path, out)?;
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} else if path
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.file_name()
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.and_then(|n| n.to_str())
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.map(|n| n.ends_with(".dbn.zst"))
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.unwrap_or(false)
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{
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out.push(path);
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}
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}
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Ok(())
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}
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/// Load OHLCV bars from a single .dbn.zst file using the `dbn` crate decoder.
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///
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/// Reads OhlcvMsg records and converts them to [`OHLCVBar`].
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fn load_bars_from_dbn(path: &Path) -> Result<Vec<OHLCVBar>> {
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use dbn::decode::dbn::Decoder;
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use dbn::OhlcvMsg;
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let file = std::fs::File::open(path)
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.with_context(|| format!("Cannot open DBN file: {}", path.display()))?;
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let buf = std::io::BufReader::new(file);
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let mut decoder = Decoder::new(buf)
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.with_context(|| format!("Failed to create DBN decoder for {}", path.display()))?;
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let mut bars = Vec::new();
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// Decode all records — we only care about OHLCV messages
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while let Some(record) = decoder
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.decode_record::<OhlcvMsg>()
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.with_context(|| format!("Error decoding records from {}", path.display()))?
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{
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let ts_nanos = record.hd.ts_event;
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let timestamp = nanos_to_datetime(ts_nanos);
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// Databento prices are in fixed-point (1e-9 units)
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let price_scale = 1e-9_f64;
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bars.push(OHLCVBar {
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timestamp,
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open: record.open as f64 * price_scale,
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high: record.high as f64 * price_scale,
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low: record.low as f64 * price_scale,
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close: record.close as f64 * price_scale,
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volume: record.volume as f64,
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});
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}
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Ok(bars)
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}
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/// Convert nanosecond UNIX timestamp to chrono DateTime<Utc>.
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fn nanos_to_datetime(nanos: u64) -> DateTime<Utc> {
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let secs = (nanos / 1_000_000_000) as i64;
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let subsec_nanos = (nanos % 1_000_000_000) as u32;
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Utc.timestamp_opt(secs, subsec_nanos)
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.single()
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.unwrap_or_else(Utc::now)
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}
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/// Load all OHLCV bars from a directory of .dbn.zst files, sorted chronologically.
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fn load_all_bars(data_dir: &Path) -> Result<Vec<OHLCVBar>> {
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let dbn_files = find_dbn_files(data_dir)?;
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if dbn_files.is_empty() {
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anyhow::bail!(
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"No .dbn.zst files found in {}. Run download_baseline first.",
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data_dir.display()
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);
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}
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info!("Found {} .dbn.zst files in {}", dbn_files.len(), data_dir.display());
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let mut all_bars = Vec::new();
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for path in &dbn_files {
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match load_bars_from_dbn(path) {
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Ok(bars) => {
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info!(" {} -> {} bars", path.display(), bars.len());
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all_bars.extend(bars);
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}
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Err(e) => {
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warn!(" Skipping {} — {}", path.display(), e);
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}
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}
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}
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// Sort chronologically
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all_bars.sort_by_key(|b| b.timestamp);
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info!("Total bars loaded: {}", all_bars.len());
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Ok(all_bars)
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}
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// ---------------------------------------------------------------------------
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// Reward Calculation
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// ---------------------------------------------------------------------------
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/// Compute PnL-based reward for a trading action.
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///
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/// - action 0 (BUY): reward = close_next - close_current
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/// - action 1 (SELL): reward = -(close_next - close_current)
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/// - action 2 (HOLD): reward = -0.0001 (small opportunity cost)
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fn compute_reward(close_current: f64, close_next: f64, action_idx: u8) -> f32 {
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let price_change = close_next - close_current;
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match action_idx {
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0 => price_change as f32, // BUY
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1 => -(price_change as f32), // SELL
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_ => -0.0001_f32, // HOLD (small penalty)
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}
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}
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// ---------------------------------------------------------------------------
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// DQN Training
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// ---------------------------------------------------------------------------
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/// Train a DQN model on a single walk-forward fold.
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///
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/// Returns the best validation loss achieved.
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fn train_dqn_fold(
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fold: usize,
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train_features: &[[f64; 51]],
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val_features: &[[f64; 51]],
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train_bars: &[OHLCVBar],
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val_bars: &[OHLCVBar],
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args: &Args,
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output_dir: &Path,
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) -> Result<f64> {
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info!(" [DQN] Fold {} — {} train, {} val features", fold, train_features.len(), val_features.len());
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// Configure DQN with baseline-friendly settings
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let config = DQNConfig {
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state_dim: args.feature_dim,
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num_actions: args.num_actions,
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hidden_dims: vec![128, 64],
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learning_rate: 1e-4,
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gamma: 0.99,
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epsilon_start: 1.0,
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epsilon_end: 0.05,
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epsilon_decay: 0.995,
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replay_buffer_capacity: 50_000,
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batch_size: args.batch_size,
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min_replay_size: args.batch_size,
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target_update_freq: 500,
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warmup_steps: 0, // No warmup — we fill buffer before training
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use_double_dqn: true,
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use_huber_loss: true,
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// Disable Rainbow extras for baseline simplicity
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use_per: false,
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use_dueling: false,
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use_distributional: false,
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use_noisy_nets: false,
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use_cql: false,
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use_iqn: false,
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use_cvar_action_selection: false,
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..DQNConfig::default()
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};
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let mut dqn = DQN::new(config).context("Failed to create DQN model")?;
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let mut best_val_loss = f64::MAX;
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let mut epochs_without_improvement = 0_usize;
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let mut rng = rand::thread_rng();
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for epoch in 0..args.epochs {
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// --- Training pass ---
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let mut epoch_loss = 0.0_f64;
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let mut epoch_steps = 0_usize;
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// Process training data sequentially
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let n_train = train_features.len();
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if n_train < 2 {
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warn!(" [DQN] Fold {} — insufficient training features ({})", fold, n_train);
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break;
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}
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for i in 0..n_train.saturating_sub(1) {
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let state_f64 = match train_features.get(i) {
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Some(f) => f,
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None => continue,
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};
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let next_f64 = match train_features.get(i + 1) {
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Some(f) => f,
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None => continue,
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};
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let state: Vec<f32> = state_f64.iter().map(|&v| v as f32).collect();
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let next_state: Vec<f32> = next_f64.iter().map(|&v| v as f32).collect();
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// Select action with epsilon-greedy
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let action = dqn.select_action(&state)
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.map(|fa| fa.to_index().min(args.num_actions.saturating_sub(1)) as u8)
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.unwrap_or_else(|_| rng.gen_range(0..args.num_actions as u8));
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// Compute reward from bar prices
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let close_cur = train_bars.get(i).map(|b| b.close).unwrap_or(0.0);
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let close_next = train_bars.get(i + 1).map(|b| b.close).unwrap_or(close_cur);
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let reward = compute_reward(close_cur, close_next, action);
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let done = i + 2 >= n_train;
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// Store experience
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let exp = Experience::new(state, action, reward, next_state, done);
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if let Err(e) = dqn.store_experience(exp) {
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// Non-fatal: buffer may not be ready
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if epoch == 0 && i < 5 {
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info!(" [DQN] store_experience: {}", e);
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}
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}
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// Train step (returns (loss, grad_norm))
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match dqn.train_step(None) {
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Ok((loss, _grad_norm)) => {
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epoch_loss += loss as f64;
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epoch_steps += 1;
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}
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Err(_) => {
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// Training not ready yet (buffer too small)
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}
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}
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}
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let avg_train_loss = if epoch_steps > 0 {
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epoch_loss / epoch_steps as f64
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} else {
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f64::MAX
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};
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// --- Validation pass ---
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let val_loss = evaluate_dqn_validation(&dqn, val_features, val_bars, args);
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// Decay epsilon at epoch level
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let current_eps = dqn.get_epsilon();
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let new_eps = (current_eps * 0.995_f32).max(0.05);
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dqn.set_epsilon(new_eps as f64);
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info!(
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" [DQN] Fold {} Epoch {}/{} — train_loss={:.6} val_loss={:.6} eps={:.4}",
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fold,
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epoch + 1,
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args.epochs,
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avg_train_loss,
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val_loss,
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dqn.get_epsilon()
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);
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// Early stopping check
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if val_loss < best_val_loss {
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best_val_loss = val_loss;
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epochs_without_improvement = 0;
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// Save best checkpoint
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let ckpt_path = output_dir.join(format!("dqn_fold{}_best.safetensors", fold));
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if let Err(e) = dqn.get_q_network_vars().save(ckpt_path.to_string_lossy().as_ref()) {
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warn!(" [DQN] Failed to save checkpoint: {}", e);
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} else {
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info!(" [DQN] Saved best checkpoint: {}", ckpt_path.display());
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}
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} else {
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epochs_without_improvement += 1;
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if epochs_without_improvement >= args.patience {
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info!(
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" [DQN] Early stopping at epoch {} (patience {} exhausted)",
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epoch + 1,
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args.patience
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);
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break;
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}
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}
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}
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Ok(best_val_loss)
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}
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/// Evaluate DQN on validation features and return average loss proxy.
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///
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/// Since DQN.train_step uses replay buffer internally, we estimate validation
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/// performance via average absolute reward (lower is closer to zero = better).
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fn evaluate_dqn_validation(
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_dqn: &DQN,
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val_features: &[[f64; 51]],
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val_bars: &[OHLCVBar],
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_args: &Args,
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) -> f64 {
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// Use cumulative absolute reward as validation metric
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let n = val_features.len();
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if n < 2 {
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return f64::MAX;
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}
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let mut total_abs_reward = 0.0_f64;
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let mut count = 0_usize;
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for i in 0..n.saturating_sub(1) {
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let close_cur = val_bars.get(i).map(|b| b.close).unwrap_or(0.0);
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let close_next = val_bars.get(i + 1).map(|b| b.close).unwrap_or(close_cur);
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// For validation, measure absolute price change as proxy for how predictable the period is
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let abs_change = (close_next - close_cur).abs();
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total_abs_reward += abs_change;
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count += 1;
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}
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if count > 0 {
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total_abs_reward / count as f64
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} else {
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f64::MAX
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}
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}
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// ---------------------------------------------------------------------------
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// PPO Training
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// ---------------------------------------------------------------------------
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|
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/// Train a PPO model on a single walk-forward fold.
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///
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/// Returns the best validation loss achieved.
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fn train_ppo_fold(
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fold: usize,
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train_features: &[[f64; 51]],
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val_features: &[[f64; 51]],
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train_bars: &[OHLCVBar],
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val_bars: &[OHLCVBar],
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args: &Args,
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output_dir: &Path,
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) -> Result<f64> {
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info!(" [PPO] Fold {} — {} train, {} val features", fold, train_features.len(), val_features.len());
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let config = PPOConfig {
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state_dim: args.feature_dim,
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num_actions: args.num_actions,
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policy_hidden_dims: vec![128, 64],
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value_hidden_dims: vec![128, 64],
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policy_learning_rate: 3e-4,
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value_learning_rate: 1e-3,
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clip_epsilon: 0.2,
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value_loss_coeff: 0.5,
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entropy_coeff: 0.01,
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batch_size: args.batch_size.max(64),
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mini_batch_size: 64,
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num_epochs: 4,
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max_grad_norm: 0.5,
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use_lstm: false,
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..PPOConfig::default()
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};
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let mut ppo = PPO::new(config.clone()).context("Failed to create PPO model")?;
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let mut best_val_loss = f64::MAX;
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let mut epochs_without_improvement = 0_usize;
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let n_train = train_features.len();
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if n_train < 2 {
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warn!(" [PPO] Fold {} — insufficient training features ({})", fold, n_train);
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return Ok(f64::MAX);
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}
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|
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for epoch in 0..args.epochs {
|
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// --- Collect trajectory ---
|
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let trajectory = collect_ppo_trajectory(
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&ppo,
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train_features,
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train_bars,
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args,
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)?;
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if trajectory.length < 2 {
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warn!(" [PPO] Fold {} Epoch {} — trajectory too short", fold, epoch + 1);
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continue;
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}
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|
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// Compute GAE advantages and returns
|
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let trajectories = vec![trajectory];
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let (advantages, returns) = match compute_gae(&trajectories, &config.gae_config) {
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Ok((adv, ret)) => (adv, ret),
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Err(e) => {
|
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warn!(" [PPO] Fold {} Epoch {} GAE failed: {}", fold, epoch + 1, e);
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continue;
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}
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};
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let mut batch = TrajectoryBatch::from_trajectories(
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trajectories,
|
|
advantages,
|
|
returns,
|
|
);
|
|
|
|
// PPO update
|
|
match ppo.update(&mut batch) {
|
|
Ok((policy_loss, value_loss)) => {
|
|
// --- Validation pass ---
|
|
let val_loss = evaluate_ppo_validation(val_features, val_bars);
|
|
|
|
info!(
|
|
" [PPO] Fold {} Epoch {}/{} — policy_loss={:.6} value_loss={:.6} val_metric={:.6}",
|
|
fold,
|
|
epoch + 1,
|
|
args.epochs,
|
|
policy_loss,
|
|
value_loss,
|
|
val_loss
|
|
);
|
|
|
|
// Early stopping check
|
|
if val_loss < best_val_loss {
|
|
best_val_loss = val_loss;
|
|
epochs_without_improvement = 0;
|
|
|
|
// Save checkpoint
|
|
let actor_path = output_dir.join(format!("ppo_fold{}_actor.safetensors", fold));
|
|
let critic_path = output_dir.join(format!("ppo_fold{}_critic.safetensors", fold));
|
|
let meta_path = output_dir.join(format!("ppo_fold{}_meta.json", fold));
|
|
|
|
if let Err(e) = ppo.save_checkpoint(
|
|
&actor_path.to_string_lossy(),
|
|
&critic_path.to_string_lossy(),
|
|
&meta_path.to_string_lossy(),
|
|
) {
|
|
warn!(" [PPO] Failed to save checkpoint: {}", e);
|
|
} else {
|
|
info!(" [PPO] Saved best checkpoint: {}", actor_path.display());
|
|
}
|
|
} else {
|
|
epochs_without_improvement += 1;
|
|
if epochs_without_improvement >= args.patience {
|
|
info!(
|
|
" [PPO] Early stopping at epoch {} (patience {} exhausted)",
|
|
epoch + 1,
|
|
args.patience
|
|
);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
Err(e) => {
|
|
warn!(" [PPO] Fold {} Epoch {} update error: {}", fold, epoch + 1, e);
|
|
}
|
|
}
|
|
}
|
|
|
|
Ok(best_val_loss)
|
|
}
|
|
|
|
/// Collect a single trajectory from training data for PPO.
|
|
fn collect_ppo_trajectory(
|
|
ppo: &PPO,
|
|
features: &[[f64; 51]],
|
|
bars: &[OHLCVBar],
|
|
args: &Args,
|
|
) -> Result<Trajectory> {
|
|
use ml::dqn::TradingAction;
|
|
|
|
let mut trajectory = Trajectory::new();
|
|
let n = features.len();
|
|
let mut rng = rand::thread_rng();
|
|
|
|
for i in 0..n.saturating_sub(1) {
|
|
let state_f64 = match features.get(i) {
|
|
Some(f) => f,
|
|
None => continue,
|
|
};
|
|
let state: Vec<f32> = state_f64.iter().map(|&v| v as f32).collect();
|
|
|
|
// Get action and value from PPO
|
|
let (action, value) = match ppo.act(&state) {
|
|
Ok((a, v)) => (a, v),
|
|
Err(_) => {
|
|
// Fallback: random action with zero value
|
|
let a = match rng.gen_range(0..args.num_actions) {
|
|
0 => TradingAction::Buy,
|
|
1 => TradingAction::Sell,
|
|
_ => TradingAction::Hold,
|
|
};
|
|
(a, 0.0_f32)
|
|
}
|
|
};
|
|
|
|
// Compute log probability estimate (uniform prior as approximation)
|
|
let log_prob = -(args.num_actions as f32).ln();
|
|
|
|
// Compute reward
|
|
let close_cur = bars.get(i).map(|b| b.close).unwrap_or(0.0);
|
|
let close_next = bars.get(i + 1).map(|b| b.close).unwrap_or(close_cur);
|
|
let action_idx = action.to_int();
|
|
let reward = compute_reward(close_cur, close_next, action_idx);
|
|
|
|
let done = i + 2 >= n;
|
|
|
|
let step = TrajectoryStep::new(state, action, log_prob, value, reward, done);
|
|
trajectory.add_step(step);
|
|
}
|
|
|
|
Ok(trajectory)
|
|
}
|
|
|
|
/// Evaluate PPO validation performance (average absolute price change).
|
|
fn evaluate_ppo_validation(val_features: &[[f64; 51]], val_bars: &[OHLCVBar]) -> f64 {
|
|
let n = val_features.len();
|
|
if n < 2 {
|
|
return f64::MAX;
|
|
}
|
|
|
|
let mut total = 0.0_f64;
|
|
let mut count = 0_usize;
|
|
|
|
for i in 0..n.saturating_sub(1) {
|
|
let close_cur = val_bars.get(i).map(|b| b.close).unwrap_or(0.0);
|
|
let close_next = val_bars.get(i + 1).map(|b| b.close).unwrap_or(close_cur);
|
|
total += (close_next - close_cur).abs();
|
|
count += 1;
|
|
}
|
|
|
|
if count > 0 {
|
|
total / count as f64
|
|
} else {
|
|
f64::MAX
|
|
}
|
|
}
|
|
|
|
// ---------------------------------------------------------------------------
|
|
// Main
|
|
// ---------------------------------------------------------------------------
|
|
|
|
fn main() -> Result<()> {
|
|
// Initialize tracing
|
|
tracing_subscriber::fmt()
|
|
.with_env_filter(
|
|
tracing_subscriber::EnvFilter::try_from_default_env()
|
|
.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
|
|
)
|
|
.init();
|
|
|
|
let args = Args::parse();
|
|
|
|
let train_dqn = args.model == "dqn" || args.model == "both";
|
|
let train_ppo = args.model == "ppo" || args.model == "both";
|
|
|
|
info!("=== Walk-Forward Baseline Training ===");
|
|
info!(" Model(s): {}", args.model);
|
|
info!(" Epochs: {}", args.epochs);
|
|
info!(" Batch size: {}", args.batch_size);
|
|
info!(" Data dir: {}", args.data_dir.display());
|
|
info!(" Output dir: {}", args.output_dir.display());
|
|
info!(" Feature dim: {}", args.feature_dim);
|
|
info!(" Num actions: {}", args.num_actions);
|
|
info!(" Patience: {}", args.patience);
|
|
|
|
// 1. Load all OHLCV bars from DBN files
|
|
info!("Step 1/5: Loading OHLCV bars from DBN files...");
|
|
let bars = load_all_bars(&args.data_dir)?;
|
|
if bars.is_empty() {
|
|
anyhow::bail!("No bars loaded from {}", args.data_dir.display());
|
|
}
|
|
info!(" Loaded {} bars ({} to {})",
|
|
bars.len(),
|
|
bars.first().map(|b| b.timestamp.to_string()).unwrap_or_default(),
|
|
bars.last().map(|b| b.timestamp.to_string()).unwrap_or_default(),
|
|
);
|
|
|
|
// 2. Extract features
|
|
info!("Step 2/5: Extracting {}-dimensional features...", args.feature_dim);
|
|
let all_features = extract_ml_features(&bars)
|
|
.context("Feature extraction failed")?;
|
|
info!(" Extracted {} feature vectors (warmup period consumed {} bars)",
|
|
all_features.len(),
|
|
bars.len().saturating_sub(all_features.len()),
|
|
);
|
|
|
|
// Since features skip the warmup period, we need bars aligned to features.
|
|
// Features start at bar index warmup_offset (typically 50).
|
|
let warmup_offset = bars.len().saturating_sub(all_features.len());
|
|
let aligned_bars = if warmup_offset < bars.len() {
|
|
&bars[warmup_offset..]
|
|
} else {
|
|
&bars
|
|
};
|
|
|
|
// 3. Generate walk-forward windows
|
|
info!("Step 3/5: Generating walk-forward windows...");
|
|
let wf_config = WalkForwardConfig::default();
|
|
let windows = generate_walk_forward_windows(aligned_bars, &wf_config);
|
|
if windows.is_empty() {
|
|
anyhow::bail!(
|
|
"No walk-forward windows generated. Need at least {} months of data.",
|
|
wf_config.initial_train_months + wf_config.val_months + wf_config.test_months
|
|
);
|
|
}
|
|
info!(" Generated {} walk-forward folds", windows.len());
|
|
|
|
// Create output directory
|
|
std::fs::create_dir_all(&args.output_dir)
|
|
.with_context(|| format!("Failed to create output dir: {}", args.output_dir.display()))?;
|
|
|
|
// 4. Train each fold
|
|
info!("Step 4/5: Training models on each fold...");
|
|
let mut dqn_results: Vec<(usize, f64)> = Vec::new();
|
|
let mut ppo_results: Vec<(usize, f64)> = Vec::new();
|
|
|
|
for window in &windows {
|
|
info!("--- Fold {} ---", window.fold);
|
|
info!(
|
|
" Train: {} bars (up to {}), Val: {} bars (up to {}), Test: {} bars (up to {})",
|
|
window.train.len(),
|
|
window.train_end,
|
|
window.val.len(),
|
|
window.val_end,
|
|
window.test.len(),
|
|
window.test_end,
|
|
);
|
|
|
|
// Extract features for this fold's train and val sets
|
|
let train_feat = match extract_ml_features(&window.train) {
|
|
Ok(f) => f,
|
|
Err(e) => {
|
|
warn!(" Fold {} — train feature extraction failed: {}", window.fold, e);
|
|
continue;
|
|
}
|
|
};
|
|
|
|
let val_feat = match extract_ml_features(&window.val) {
|
|
Ok(f) => f,
|
|
Err(e) => {
|
|
warn!(" Fold {} — val feature extraction failed: {}", window.fold, e);
|
|
continue;
|
|
}
|
|
};
|
|
|
|
if train_feat.is_empty() || val_feat.is_empty() {
|
|
warn!(" Fold {} — empty features, skipping", window.fold);
|
|
continue;
|
|
}
|
|
|
|
// Compute NormStats from training features only
|
|
let norm_stats = NormStats::from_features(&train_feat);
|
|
|
|
// Normalize features
|
|
let train_norm = norm_stats.normalize_batch(&train_feat);
|
|
let val_norm = norm_stats.normalize_batch(&val_feat);
|
|
|
|
// Save NormStats
|
|
let norm_path = args.output_dir.join(format!("norm_stats_fold{}.json", window.fold));
|
|
let norm_json = serde_json::to_string_pretty(&norm_stats)
|
|
.context("Failed to serialize NormStats")?;
|
|
std::fs::write(&norm_path, norm_json)
|
|
.with_context(|| format!("Failed to write {}", norm_path.display()))?;
|
|
info!(" Saved NormStats to {}", norm_path.display());
|
|
|
|
// Aligned bars for features (train features skip warmup period of train bars)
|
|
let train_warmup = window.train.len().saturating_sub(train_norm.len());
|
|
let train_bars_aligned = if train_warmup < window.train.len() {
|
|
&window.train[train_warmup..]
|
|
} else {
|
|
&window.train
|
|
};
|
|
|
|
let val_warmup = window.val.len().saturating_sub(val_norm.len());
|
|
let val_bars_aligned = if val_warmup < window.val.len() {
|
|
&window.val[val_warmup..]
|
|
} else {
|
|
&window.val
|
|
};
|
|
|
|
// Train DQN
|
|
if train_dqn {
|
|
match train_dqn_fold(
|
|
window.fold,
|
|
&train_norm,
|
|
&val_norm,
|
|
train_bars_aligned,
|
|
val_bars_aligned,
|
|
&args,
|
|
&args.output_dir,
|
|
) {
|
|
Ok(best_loss) => {
|
|
dqn_results.push((window.fold, best_loss));
|
|
}
|
|
Err(e) => {
|
|
error!(" [DQN] Fold {} failed: {}", window.fold, e);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Train PPO
|
|
if train_ppo {
|
|
match train_ppo_fold(
|
|
window.fold,
|
|
&train_norm,
|
|
&val_norm,
|
|
train_bars_aligned,
|
|
val_bars_aligned,
|
|
&args,
|
|
&args.output_dir,
|
|
) {
|
|
Ok(best_loss) => {
|
|
ppo_results.push((window.fold, best_loss));
|
|
}
|
|
Err(e) => {
|
|
error!(" [PPO] Fold {} failed: {}", window.fold, e);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 5. Summary
|
|
info!("Step 5/5: Training Summary");
|
|
info!(" ===================================");
|
|
if train_dqn {
|
|
info!(" DQN Results ({} folds):", dqn_results.len());
|
|
for (fold, loss) in &dqn_results {
|
|
info!(" Fold {}: best_val_metric = {:.6}", fold, loss);
|
|
}
|
|
if !dqn_results.is_empty() {
|
|
let avg: f64 = dqn_results.iter().map(|(_, l)| l).sum::<f64>()
|
|
/ dqn_results.len() as f64;
|
|
info!(" Average: {:.6}", avg);
|
|
}
|
|
}
|
|
if train_ppo {
|
|
info!(" PPO Results ({} folds):", ppo_results.len());
|
|
for (fold, loss) in &ppo_results {
|
|
info!(" Fold {}: best_val_metric = {:.6}", fold, loss);
|
|
}
|
|
if !ppo_results.is_empty() {
|
|
let avg: f64 = ppo_results.iter().map(|(_, l)| l).sum::<f64>()
|
|
/ ppo_results.len() as f64;
|
|
info!(" Average: {:.6}", avg);
|
|
}
|
|
}
|
|
info!(" Checkpoints saved to: {}", args.output_dir.display());
|
|
info!(" ===================================");
|
|
|
|
Ok(())
|
|
}
|