f946dcd952
WAVE 22: All examples, benchmarks, and data loaders updated Files Modified (41 files): - DQN examples: 7 files (train_dqn, evaluate_dqn, validate_dqn, etc.) - PPO examples: 6 files (train_ppo, continuous_ppo, benchmark_ppo, etc.) - TFT examples: 9 files (train_tft, validate_tft, benchmark_tft, etc.) - MAMBA-2 examples: 3 files (train_mamba2, verify_dimensions, etc.) - Benchmarks: 5 files (cuda_speedup, weight_caching, future_decoder, etc.) - Data loaders: 7 files (parquet_utils, dbn_sequence_loader, tlob_loader, etc.) - Integration: 4 files (load_parquet_data, streaming loaders, etc.) Key Changes: - state_dim: 225 → 54 (DQN, PPO) - input_dim: 225 → 54 (TFT) - d_model: 225 → 54 (MAMBA-2) - Memory: 1.8KB → 0.43KB per vector (76% reduction) - All tensor shapes updated: (batch, 225) → (batch, 54) Agents Deployed: 5 parallel agents Validation: cargo check PASSING Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
884 lines
31 KiB
Rust
884 lines
31 KiB
Rust
//! DQN Backtest Validation Script
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//!
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//! Validates trained DQN checkpoints against production criteria:
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//! - Sharpe ratio > 2.0
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//! - Win rate > 55%
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//! - Max drawdown < 20%
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//!
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//! # Usage
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//!
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//! ```bash
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//! # Evaluate single checkpoint
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//! cargo run -p ml --example backtest_dqn --release --features cuda -- \
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//! --checkpoint ml/trained_models/dqn_epoch_5.safetensors \
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//! --data test_data/ES_FUT_180d.parquet
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//!
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//! # Compare against baseline
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//! cargo run -p ml --example backtest_dqn --release --features cuda -- \
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//! --checkpoint ml/trained_models/dqn_epoch_5.safetensors \
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//! --baseline ml/trained_models/dqn_baseline.safetensors \
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//! --data test_data/ES_FUT_180d.parquet
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//!
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//! # Export results to JSON
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//! cargo run -p ml --example backtest_dqn --release --features cuda -- \
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//! --checkpoint ml/trained_models/dqn_epoch_5.safetensors \
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//! --data test_data/ES_FUT_180d.parquet \
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//! --output-json backtest_results.json
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//!
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//! # Export markdown report
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//! cargo run -p ml --example backtest_dqn --release --features cuda -- \
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//! --checkpoint ml/trained_models/dqn_epoch_5.safetensors \
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//! --baseline ml/trained_models/dqn_baseline.safetensors \
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//! --data test_data/ES_FUT_180d.parquet \
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//! --output-markdown backtest_report.md
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//! ```
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//!
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//! # Architecture
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//!
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//! ```text
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//! ┌─────────────────────────────────────────────────────────────────┐
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//! │ BACKTEST VALIDATION PIPELINE │
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//! │ │
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//! │ 1. LOAD CHECKPOINTS │
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//! │ ├─ Primary checkpoint (required) │
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//! │ └─ Baseline checkpoint (optional) │
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//! │ │
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//! │ 2. LOAD VALIDATION DATA │
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//! │ ├─ Parquet file → OHLCV bars │
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//! │ └─ Extract 128-dim features (Wave D) │
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//! │ │
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//! │ 3. RUN BACKTEST │
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//! │ ├─ Primary model inference (greedy actions) │
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//! │ ├─ Baseline model inference (if provided) │
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//! │ ├─ Execute trades via EvaluationEngine │
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//! │ └─ Record trade history │
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//! │ │
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//! │ 4. CALCULATE METRICS │
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//! │ ├─ Sharpe ratio (risk-adjusted return) │
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//! │ ├─ Win rate (% profitable trades) │
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//! │ ├─ Max drawdown (peak-to-trough decline) │
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//! │ └─ Total return (% gain/loss) │
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//! │ │
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//! │ 5. VALIDATE CRITERIA │
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//! │ ├─ Sharpe > 2.0 ✅/❌ │
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//! │ ├─ Win rate > 55% ✅/❌ │
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//! │ └─ Drawdown < 20% ✅/❌ │
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//! │ │
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//! │ 6. GENERATE REPORT │
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//! │ ├─ Console output (always) │
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//! │ ├─ JSON output (--output-json) │
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//! │ └─ Markdown report (--output-markdown) │
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//! └─────────────────────────────────────────────────────────────────┘
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//! ```
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use anyhow::{Context, Result};
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use candle_core::{Device, Tensor};
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use clap::Parser;
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use serde::{Deserialize, Serialize};
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use std::path::{Path, PathBuf};
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use std::time::Instant;
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use tracing::{info, warn};
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use tracing_subscriber::FmtSubscriber;
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use ml::data_loaders::load_parquet_data_with_timestamps;
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use ml::dqn::dqn::{WorkingDQN, WorkingDQNConfig};
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use ml::evaluation::{EvaluationEngine, PerformanceMetrics};
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use ml::features::extraction::OHLCVBar;
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use ml::preprocessing::{preprocess_prices, PreprocessConfig};
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// ============================================================================
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// CLI Configuration
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// ============================================================================
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/// Backtest validation configuration
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#[derive(Parser, Debug)]
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#[command(
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name = "backtest_dqn",
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about = "Validate DQN checkpoint against production criteria",
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long_about = "Comprehensive backtest validation pipeline with Sharpe, win rate, \
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and drawdown metrics. Compares against baseline if provided."
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)]
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struct BacktestConfig {
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/// Path to DQN checkpoint to validate (SafeTensors format)
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#[arg(long, required = true)]
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checkpoint: PathBuf,
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/// Path to baseline checkpoint for comparison (optional)
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#[arg(long)]
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baseline: Option<PathBuf>,
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/// Path to validation data (Parquet format)
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#[arg(long, required = true)]
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data: PathBuf,
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/// Device selection: cpu, cuda, or auto
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#[arg(long, default_value = "auto")]
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device: String,
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/// Initial capital for backtest (default: $100,000)
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#[arg(long, default_value = "100000.0")]
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initial_capital: f32,
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/// Warmup bars to skip (insufficient feature history)
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#[arg(long, default_value = "50")]
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warmup_bars: usize,
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/// Output JSON file path (optional)
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#[arg(long)]
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output_json: Option<PathBuf>,
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/// Output markdown report path (optional)
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#[arg(long)]
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output_markdown: Option<PathBuf>,
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/// Verbose logging (DEBUG level)
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#[arg(short, long)]
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verbose: bool,
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/// Success criteria: minimum Sharpe ratio
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#[arg(long, default_value = "2.0")]
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min_sharpe: f64,
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/// Success criteria: minimum win rate (%)
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#[arg(long, default_value = "55.0")]
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min_win_rate: f64,
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/// Success criteria: maximum drawdown (%)
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#[arg(long, default_value = "20.0")]
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max_drawdown: f64,
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}
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impl BacktestConfig {
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/// Validate configuration parameters
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fn validate(&self) -> Result<()> {
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// Validate checkpoint exists
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if !self.checkpoint.exists() {
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anyhow::bail!(
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"Checkpoint file not found: {}\n\
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Suggestion: Train a model first using train_dqn example",
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self.checkpoint.display()
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);
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}
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// Validate baseline (if specified)
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if let Some(ref baseline) = self.baseline {
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if !baseline.exists() {
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anyhow::bail!("Baseline checkpoint not found: {}", baseline.display());
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}
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}
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// Validate data file
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if !self.data.exists() {
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anyhow::bail!(
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"Data file not found: {}\n\
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Suggestion: Use test_data/ES_FUT_180d.parquet",
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self.data.display()
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);
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}
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// Validate device string
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if !["cpu", "cuda", "auto"].contains(&self.device.as_str()) {
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anyhow::bail!(
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"Invalid device: '{}'. Must be: cpu, cuda, or auto",
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self.device
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);
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}
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// Validate output paths (if specified)
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if let Some(ref output_path) = self.output_json {
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if let Some(parent) = output_path.parent() {
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if !parent.exists() {
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anyhow::bail!(
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"Output JSON directory does not exist: {}\n\
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Suggestion: mkdir -p {}",
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parent.display(),
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parent.display()
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);
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}
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}
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}
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if let Some(ref output_path) = self.output_markdown {
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if let Some(parent) = output_path.parent() {
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if !parent.exists() {
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anyhow::bail!(
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"Output markdown directory does not exist: {}\n\
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Suggestion: mkdir -p {}",
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parent.display(),
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parent.display()
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);
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}
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}
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}
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Ok(())
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}
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}
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// ============================================================================
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// Model Loading
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// ============================================================================
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/// Load DQN checkpoint from SafeTensors file
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fn load_checkpoint(path: &Path, _device: &Device) -> Result<WorkingDQN> {
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info!("📦 Loading checkpoint: {}", path.display());
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// Create WorkingDQN configuration (matches training architecture)
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let config = WorkingDQNConfig {
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state_dim: 54, // 54 features (Wave 21 feature reduction)
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hidden_dims: vec![256, 128, 64], // Match trainer architecture
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num_actions: 3,
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learning_rate: 0.001,
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gamma: 0.99,
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epsilon_start: 0.0, // No exploration during evaluation
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epsilon_end: 0.0,
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epsilon_decay: 1.0,
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replay_buffer_capacity: 1000,
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batch_size: 32,
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min_replay_size: 64,
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target_update_freq: 1000,
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use_double_dqn: true,
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use_huber_loss: true,
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huber_delta: 1.0,
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leaky_relu_alpha: 0.01,
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gradient_clip_norm: 10.0,
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tau: 1.0,
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use_soft_updates: false,
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warmup_steps: 0,
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};
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// Create model
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let mut dqn = WorkingDQN::new(config).context("Failed to create WorkingDQN")?;
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// Load weights
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let path_str = path
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.to_str()
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.ok_or_else(|| anyhow::anyhow!("Path contains invalid UTF-8"))?;
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dqn.load_from_safetensors(path_str)
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.context(format!("Failed to load checkpoint: {}", path.display()))?;
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info!("✅ Checkpoint loaded successfully");
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Ok(dqn)
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}
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// ============================================================================
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// Backtest Execution
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// ============================================================================
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/// Run backtest on validation data
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fn run_backtest(
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dqn: &mut WorkingDQN,
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features: &[[f64; 128]],
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bars: &[OHLCVBar],
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initial_capital: f32,
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) -> Result<PerformanceMetrics> {
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info!("🔄 Running backtest ({} bars)...", bars.len());
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if features.len() != bars.len() {
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anyhow::bail!(
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"Feature/bar mismatch: {} features, {} bars",
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features.len(),
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bars.len()
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);
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}
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let mut engine = EvaluationEngine::new(initial_capital);
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let start = Instant::now();
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// Run inference for each bar
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for (i, (feature_vec, bar)) in features.iter().zip(bars.iter()).enumerate() {
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// Convert f64 features to f32
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let state_f32: Vec<f32> = feature_vec.iter().map(|&x| x as f32).collect();
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// Get greedy action (epsilon=0)
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let trading_action = dqn
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.select_action(state_f32.as_slice())
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.context(format!("Inference failed at bar {}", i))?;
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// Convert to evaluation Action enum
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let action = match trading_action.to_index() {
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0 => ml::evaluation::engine::Action::Buy,
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1 => ml::evaluation::engine::Action::Hold,
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2 => ml::evaluation::engine::Action::Sell,
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_ => ml::evaluation::engine::Action::Hold,
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};
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// Convert OHLCVBar to evaluation OHLCVBar
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let eval_bar = ml::evaluation::metrics::OHLCVBar {
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timestamp: bar.timestamp.timestamp(),
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open: bar.open as f32,
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high: bar.high as f32,
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low: bar.low as f32,
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close: bar.close as f32,
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volume: bar.volume as f32,
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};
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// Process bar
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engine.process_bar(i, &eval_bar, action);
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}
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// Close any open position at end
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if engine.current_position.is_some() {
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let last_bar = &bars[bars.len() - 1];
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let eval_bar = ml::evaluation::metrics::OHLCVBar {
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timestamp: last_bar.timestamp.timestamp(),
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open: last_bar.open as f32,
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high: last_bar.high as f32,
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low: last_bar.low as f32,
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close: last_bar.close as f32,
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volume: last_bar.volume as f32,
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};
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engine.close_position(bars.len() - 1, &eval_bar);
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}
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let elapsed = start.elapsed();
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info!("✅ Backtest complete ({:.2}s)", elapsed.as_secs_f64());
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// Calculate performance metrics
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let eval_bars: Vec<ml::evaluation::metrics::OHLCVBar> = bars
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.iter()
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.map(|b| ml::evaluation::metrics::OHLCVBar {
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timestamp: b.timestamp.timestamp(),
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open: b.open as f32,
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high: b.high as f32,
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low: b.low as f32,
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close: b.close as f32,
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volume: b.volume as f32,
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})
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.collect();
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let metrics = PerformanceMetrics::from_trades(&engine.trades, initial_capital, &eval_bars);
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// Log action distribution
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let dist = engine.get_action_distribution();
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info!("📊 Action Distribution:");
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info!(" BUY: {} ({:.1}%)", dist.buy_count, dist.buy_pct);
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info!(" HOLD: {} ({:.1}%)", dist.hold_count, dist.hold_pct);
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info!(" SELL: {} ({:.1}%)", dist.sell_count, dist.sell_pct);
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Ok(metrics)
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}
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// ============================================================================
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// Validation & Reporting
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// ============================================================================
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/// Backtest validation result
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#[derive(Debug, Clone, Serialize, Deserialize)]
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struct ValidationResult {
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checkpoint_name: String,
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baseline_name: Option<String>,
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metrics: PerformanceMetrics,
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baseline_metrics: Option<PerformanceMetrics>,
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success_criteria: SuccessCriteria,
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verdict: Verdict,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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struct SuccessCriteria {
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min_sharpe: f64,
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min_win_rate: f64,
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max_drawdown: f64,
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sharpe_passed: bool,
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win_rate_passed: bool,
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drawdown_passed: bool,
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overall_passed: bool,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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enum Verdict {
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ProductionReady,
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Failed { reasons: Vec<String> },
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}
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/// Validate backtest results against production criteria
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fn validate_results(
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checkpoint_name: String,
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baseline_name: Option<String>,
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metrics: PerformanceMetrics,
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baseline_metrics: Option<PerformanceMetrics>,
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config: &BacktestConfig,
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) -> ValidationResult {
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info!("📋 Validating against production criteria...");
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let sharpe_passed = metrics.sharpe_ratio >= config.min_sharpe;
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let win_rate_passed = metrics.win_rate >= config.min_win_rate;
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let drawdown_passed = metrics.max_drawdown_pct <= config.max_drawdown;
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let overall_passed = sharpe_passed && win_rate_passed && drawdown_passed;
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let mut reasons = Vec::new();
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if !sharpe_passed {
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reasons.push(format!(
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"Sharpe ratio {:.2} < {:.2} (required)",
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metrics.sharpe_ratio, config.min_sharpe
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));
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}
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if !win_rate_passed {
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reasons.push(format!(
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"Win rate {:.1}% < {:.1}% (required)",
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metrics.win_rate, config.min_win_rate
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));
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}
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if !drawdown_passed {
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reasons.push(format!(
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"Max drawdown {:.1}% > {:.1}% (limit)",
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metrics.max_drawdown_pct, config.max_drawdown
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));
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}
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let verdict = if overall_passed {
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Verdict::ProductionReady
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} else {
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Verdict::Failed { reasons }
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};
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ValidationResult {
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checkpoint_name,
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baseline_name,
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metrics,
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baseline_metrics,
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success_criteria: SuccessCriteria {
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min_sharpe: config.min_sharpe,
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min_win_rate: config.min_win_rate,
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max_drawdown: config.max_drawdown,
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sharpe_passed,
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win_rate_passed,
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drawdown_passed,
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overall_passed,
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},
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verdict,
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}
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}
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/// Print validation report to console
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fn print_report(result: &ValidationResult) {
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println!("\n╔══════════════════════════════════════════════════════════════════════╗");
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println!("║ DQN BACKTEST VALIDATION REPORT ║");
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println!("╚══════════════════════════════════════════════════════════════════════╝");
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println!();
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// Checkpoint info
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println!("═══ Checkpoint ═══");
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println!(" Primary: {}", result.checkpoint_name);
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if let Some(ref baseline) = result.baseline_name {
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println!(" Baseline: {}", baseline);
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}
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println!();
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// Performance metrics
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println!("═══ Performance Metrics ═══");
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println!(
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" Total Return: {:>8.2}%",
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result.metrics.total_return_pct
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);
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println!(" Sharpe Ratio: {:>8.2}", result.metrics.sharpe_ratio);
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println!(
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" Max Drawdown: {:>8.2}%",
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result.metrics.max_drawdown_pct
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);
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println!(" Win Rate: {:>8.1}%", result.metrics.win_rate);
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println!(" Total Trades: {:>8}", result.metrics.total_trades);
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println!(" Avg Trade PnL: {:>8.2}", result.metrics.avg_trade_pnl);
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println!(" Final Equity: {:>8.2}", result.metrics.final_equity);
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println!();
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// Baseline comparison (if available)
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if let Some(ref baseline) = result.baseline_metrics {
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println!("═══ Baseline Comparison ═══");
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let sharpe_diff = result.metrics.sharpe_ratio - baseline.sharpe_ratio;
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let return_diff = result.metrics.total_return_pct - baseline.total_return_pct;
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let drawdown_diff = baseline.max_drawdown_pct - result.metrics.max_drawdown_pct;
|
|
let win_rate_diff = result.metrics.win_rate - baseline.win_rate;
|
|
|
|
println!(
|
|
" Sharpe Ratio: {:>8.2} → {:>8.2} ({:+.2})",
|
|
baseline.sharpe_ratio, result.metrics.sharpe_ratio, sharpe_diff
|
|
);
|
|
println!(
|
|
" Total Return: {:>8.2}% → {:>8.2}% ({:+.2}%)",
|
|
baseline.total_return_pct, result.metrics.total_return_pct, return_diff
|
|
);
|
|
println!(
|
|
" Max Drawdown: {:>8.2}% → {:>8.2}% ({:+.2}%)",
|
|
baseline.max_drawdown_pct, result.metrics.max_drawdown_pct, drawdown_diff
|
|
);
|
|
println!(
|
|
" Win Rate: {:>8.1}% → {:>8.1}% ({:+.1}%)",
|
|
baseline.win_rate, result.metrics.win_rate, win_rate_diff
|
|
);
|
|
println!();
|
|
}
|
|
|
|
// Success criteria
|
|
println!("═══ Success Criteria ═══");
|
|
println!(
|
|
" {} Sharpe Ratio ≥ {:.2}: {}",
|
|
if result.success_criteria.sharpe_passed {
|
|
"✅"
|
|
} else {
|
|
"❌"
|
|
},
|
|
result.success_criteria.min_sharpe,
|
|
result.metrics.sharpe_ratio
|
|
);
|
|
println!(
|
|
" {} Win Rate ≥ {:.1}%: {:.1}%",
|
|
if result.success_criteria.win_rate_passed {
|
|
"✅"
|
|
} else {
|
|
"❌"
|
|
},
|
|
result.success_criteria.min_win_rate,
|
|
result.metrics.win_rate
|
|
);
|
|
println!(
|
|
" {} Max Drawdown ≤ {:.1}%: {:.1}%",
|
|
if result.success_criteria.drawdown_passed {
|
|
"✅"
|
|
} else {
|
|
"❌"
|
|
},
|
|
result.success_criteria.max_drawdown,
|
|
result.metrics.max_drawdown_pct
|
|
);
|
|
println!();
|
|
|
|
// Verdict
|
|
println!("═══ Verdict ═══");
|
|
match &result.verdict {
|
|
Verdict::ProductionReady => {
|
|
println!(" ✅ PRODUCTION READY");
|
|
println!(" Checkpoint meets all success criteria");
|
|
},
|
|
Verdict::Failed { reasons } => {
|
|
println!(" ❌ FAILED VALIDATION");
|
|
println!(" Reasons:");
|
|
for reason in reasons {
|
|
println!(" • {}", reason);
|
|
}
|
|
},
|
|
}
|
|
println!();
|
|
}
|
|
|
|
/// Generate markdown report
|
|
fn generate_markdown(result: &ValidationResult) -> String {
|
|
let mut md = String::new();
|
|
|
|
md.push_str("# DQN Backtest Validation Report\n\n");
|
|
|
|
// Checkpoint info
|
|
md.push_str("## Checkpoint\n\n");
|
|
md.push_str(&format!("**Primary**: `{}`\n", result.checkpoint_name));
|
|
if let Some(ref baseline) = result.baseline_name {
|
|
md.push_str(&format!("**Baseline**: `{}`\n", baseline));
|
|
}
|
|
md.push_str("\n");
|
|
|
|
// Performance metrics
|
|
md.push_str("## Performance Metrics\n\n");
|
|
md.push_str("| Metric | Value |\n");
|
|
md.push_str("|--------|-------|\n");
|
|
md.push_str(&format!(
|
|
"| Total Return | {:.2}% |\n",
|
|
result.metrics.total_return_pct
|
|
));
|
|
md.push_str(&format!(
|
|
"| Sharpe Ratio | {:.2} |\n",
|
|
result.metrics.sharpe_ratio
|
|
));
|
|
md.push_str(&format!(
|
|
"| Max Drawdown | {:.2}% |\n",
|
|
result.metrics.max_drawdown_pct
|
|
));
|
|
md.push_str(&format!("| Win Rate | {:.1}% |\n", result.metrics.win_rate));
|
|
md.push_str(&format!(
|
|
"| Total Trades | {} |\n",
|
|
result.metrics.total_trades
|
|
));
|
|
md.push_str(&format!(
|
|
"| Avg Trade PnL | {:.2} |\n",
|
|
result.metrics.avg_trade_pnl
|
|
));
|
|
md.push_str(&format!(
|
|
"| Final Equity | {:.2} |\n",
|
|
result.metrics.final_equity
|
|
));
|
|
md.push_str("\n");
|
|
|
|
// Baseline comparison
|
|
if let Some(ref baseline) = result.baseline_metrics {
|
|
md.push_str("## Baseline Comparison\n\n");
|
|
md.push_str("| Metric | Baseline | Primary | Change |\n");
|
|
md.push_str("|--------|----------|---------|--------|\n");
|
|
|
|
let sharpe_diff = result.metrics.sharpe_ratio - baseline.sharpe_ratio;
|
|
let return_diff = result.metrics.total_return_pct - baseline.total_return_pct;
|
|
let drawdown_diff = baseline.max_drawdown_pct - result.metrics.max_drawdown_pct;
|
|
let win_rate_diff = result.metrics.win_rate - baseline.win_rate;
|
|
|
|
md.push_str(&format!(
|
|
"| Sharpe Ratio | {:.2} | {:.2} | {:+.2} |\n",
|
|
baseline.sharpe_ratio, result.metrics.sharpe_ratio, sharpe_diff
|
|
));
|
|
md.push_str(&format!(
|
|
"| Total Return | {:.2}% | {:.2}% | {:+.2}% |\n",
|
|
baseline.total_return_pct, result.metrics.total_return_pct, return_diff
|
|
));
|
|
md.push_str(&format!(
|
|
"| Max Drawdown | {:.2}% | {:.2}% | {:+.2}% |\n",
|
|
baseline.max_drawdown_pct, result.metrics.max_drawdown_pct, drawdown_diff
|
|
));
|
|
md.push_str(&format!(
|
|
"| Win Rate | {:.1}% | {:.1}% | {:+.1}% |\n",
|
|
baseline.win_rate, result.metrics.win_rate, win_rate_diff
|
|
));
|
|
md.push_str("\n");
|
|
}
|
|
|
|
// Success criteria
|
|
md.push_str("## Success Criteria\n\n");
|
|
md.push_str(&format!(
|
|
"- {} **Sharpe Ratio ≥ {:.2}**: {:.2}\n",
|
|
if result.success_criteria.sharpe_passed {
|
|
"✅"
|
|
} else {
|
|
"❌"
|
|
},
|
|
result.success_criteria.min_sharpe,
|
|
result.metrics.sharpe_ratio
|
|
));
|
|
md.push_str(&format!(
|
|
"- {} **Win Rate ≥ {:.1}%**: {:.1}%\n",
|
|
if result.success_criteria.win_rate_passed {
|
|
"✅"
|
|
} else {
|
|
"❌"
|
|
},
|
|
result.success_criteria.min_win_rate,
|
|
result.metrics.win_rate
|
|
));
|
|
md.push_str(&format!(
|
|
"- {} **Max Drawdown ≤ {:.1}%**: {:.1}%\n",
|
|
if result.success_criteria.drawdown_passed {
|
|
"✅"
|
|
} else {
|
|
"❌"
|
|
},
|
|
result.success_criteria.max_drawdown,
|
|
result.metrics.max_drawdown_pct
|
|
));
|
|
md.push_str("\n");
|
|
|
|
// Verdict
|
|
md.push_str("## Verdict\n\n");
|
|
match &result.verdict {
|
|
Verdict::ProductionReady => {
|
|
md.push_str("✅ **PRODUCTION READY**\n\n");
|
|
md.push_str(
|
|
"Checkpoint meets all success criteria and is ready for production deployment.\n",
|
|
);
|
|
},
|
|
Verdict::Failed { reasons } => {
|
|
md.push_str("❌ **FAILED VALIDATION**\n\n");
|
|
md.push_str("Checkpoint failed the following criteria:\n\n");
|
|
for reason in reasons {
|
|
md.push_str(&format!("- {}\n", reason));
|
|
}
|
|
},
|
|
}
|
|
|
|
md
|
|
}
|
|
|
|
// ============================================================================
|
|
// Main Orchestrator
|
|
// ============================================================================
|
|
|
|
#[tokio::main]
|
|
async fn main() -> Result<()> {
|
|
// Parse CLI options
|
|
let config = BacktestConfig::parse();
|
|
|
|
// Setup logging
|
|
let level = if config.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")?;
|
|
|
|
// Print banner
|
|
info!("╔══════════════════════════════════════════════════════════════════════╗");
|
|
info!("║ DQN Backtest Validation Pipeline - v1.0.0 ║");
|
|
info!(
|
|
"║ Timestamp: {} ║",
|
|
chrono::Local::now().format("%Y-%m-%d %H:%M:%S")
|
|
);
|
|
info!("╚══════════════════════════════════════════════════════════════════════╝");
|
|
info!("");
|
|
|
|
// Validate configuration
|
|
info!("🔍 Validating configuration...");
|
|
config
|
|
.validate()
|
|
.context("Configuration validation failed")?;
|
|
info!("✅ Configuration validated");
|
|
info!("");
|
|
|
|
// Create device
|
|
let device = match config.device.as_str() {
|
|
"cpu" => {
|
|
info!("🖥️ Using CPU device");
|
|
Device::Cpu
|
|
},
|
|
"cuda" => {
|
|
info!("🎮 Using CUDA device");
|
|
Device::new_cuda(0).context("CUDA unavailable. Use --device cpu or --device auto")?
|
|
},
|
|
"auto" => match Device::cuda_if_available(0) {
|
|
Ok(cuda_device) => {
|
|
info!("🎮 Using CUDA device (auto-detected)");
|
|
cuda_device
|
|
},
|
|
Err(_) => {
|
|
warn!("⚠️ CUDA unavailable, falling back to CPU");
|
|
Device::Cpu
|
|
},
|
|
},
|
|
_ => unreachable!(),
|
|
};
|
|
info!("");
|
|
|
|
let total_start = Instant::now();
|
|
|
|
// Phase 1: Load data
|
|
info!("📂 Phase 1: Loading validation data");
|
|
let data_start = Instant::now();
|
|
let (mut features, _timestamps, bars) =
|
|
load_parquet_data_with_timestamps(&config.data, config.warmup_bars)
|
|
.context("Failed to load validation data")?;
|
|
info!(
|
|
"✅ Loaded {} bars ({:.2}s)",
|
|
bars.len(),
|
|
data_start.elapsed().as_secs_f64()
|
|
);
|
|
info!("");
|
|
|
|
// Phase 1.5: Preprocessing
|
|
info!("🔬 Phase 1.5: Preprocessing data");
|
|
let close_prices: Vec<f32> = bars.iter().map(|b| b.close as f32).collect();
|
|
let close_tensor = Tensor::from_slice(&close_prices, (close_prices.len(),), &device)
|
|
.context("Failed to create tensor")?;
|
|
|
|
let preprocess_config = PreprocessConfig {
|
|
window_size: 50,
|
|
clip_sigma: 5.0,
|
|
use_log_returns: true,
|
|
};
|
|
|
|
let preprocessed =
|
|
preprocess_prices(&close_tensor, preprocess_config).context("Preprocessing failed")?;
|
|
let preprocessed_vec: Vec<f32> = preprocessed
|
|
.to_vec1()
|
|
.context("Failed to convert tensor to vec")?;
|
|
|
|
// Update features with preprocessed close prices
|
|
for (i, feature_vec) in features.iter_mut().enumerate() {
|
|
if i < preprocessed_vec.len() {
|
|
feature_vec[3] = preprocessed_vec[i] as f64; // Index 3 is close price
|
|
}
|
|
}
|
|
info!("✅ Preprocessing complete");
|
|
info!("");
|
|
|
|
// Phase 2: Load checkpoints
|
|
info!("📦 Phase 2: Loading checkpoints");
|
|
let mut primary_dqn = load_checkpoint(&config.checkpoint, &device)?;
|
|
let mut baseline_dqn = if let Some(ref baseline_path) = config.baseline {
|
|
Some(load_checkpoint(baseline_path, &device)?)
|
|
} else {
|
|
None
|
|
};
|
|
info!("");
|
|
|
|
// Phase 3: Run backtests
|
|
info!("🔄 Phase 3: Running backtests");
|
|
|
|
// Primary backtest
|
|
let primary_metrics = run_backtest(&mut primary_dqn, &features, &bars, config.initial_capital)?;
|
|
|
|
// Baseline backtest (if provided)
|
|
let baseline_metrics = if let Some(ref mut baseline) = baseline_dqn {
|
|
info!("");
|
|
info!("🔄 Running baseline backtest...");
|
|
Some(run_backtest(
|
|
baseline,
|
|
&features,
|
|
&bars,
|
|
config.initial_capital,
|
|
)?)
|
|
} else {
|
|
None
|
|
};
|
|
info!("");
|
|
|
|
// Phase 4: Validate results
|
|
info!("📋 Phase 4: Validating results");
|
|
let result = validate_results(
|
|
config.checkpoint.display().to_string(),
|
|
config.baseline.as_ref().map(|p| p.display().to_string()),
|
|
primary_metrics,
|
|
baseline_metrics,
|
|
&config,
|
|
);
|
|
info!("");
|
|
|
|
// Phase 5: Generate reports
|
|
info!("📝 Phase 5: Generating reports");
|
|
|
|
// Console output (always)
|
|
print_report(&result);
|
|
|
|
// JSON output (if requested)
|
|
if let Some(ref json_path) = config.output_json {
|
|
let json = serde_json::to_string_pretty(&result).context("Failed to serialize to JSON")?;
|
|
std::fs::write(json_path, json)
|
|
.context(format!("Failed to write JSON: {}", json_path.display()))?;
|
|
info!("✅ JSON report saved: {}", json_path.display());
|
|
}
|
|
|
|
// Markdown output (if requested)
|
|
if let Some(ref md_path) = config.output_markdown {
|
|
let markdown = generate_markdown(&result);
|
|
std::fs::write(md_path, markdown)
|
|
.context(format!("Failed to write markdown: {}", md_path.display()))?;
|
|
info!("✅ Markdown report saved: {}", md_path.display());
|
|
}
|
|
|
|
let total_elapsed = total_start.elapsed();
|
|
info!("");
|
|
info!("╔══════════════════════════════════════════════════════════════════════╗");
|
|
info!("║ VALIDATION COMPLETE ║");
|
|
info!("╚══════════════════════════════════════════════════════════════════════╝");
|
|
info!(" Total runtime: {:.2}s", total_elapsed.as_secs_f64());
|
|
info!("");
|
|
|
|
// Exit with appropriate code
|
|
match result.verdict {
|
|
Verdict::ProductionReady => {
|
|
info!("✅ EXIT CODE 0: Production ready");
|
|
std::process::exit(0);
|
|
},
|
|
Verdict::Failed { .. } => {
|
|
warn!("❌ EXIT CODE 1: Validation failed");
|
|
std::process::exit(1);
|
|
},
|
|
}
|
|
}
|