31c610b69a
Phase 1 — Training quality (14 structural bugs): - Reward: sqrt micro-reward scaling, remove sparse normalization, Kelly penalty, additive OFI, magnitude-scaled capital floor penalty - Gradient: CQL 25→10%, IQN 60→75%, MSE magnitude 4×, 3-phase counterfactual, tau_anneal 100K→500K, epsilon floor 2%, PopArt fold reset, dd_threshold 0.5% - Monitoring: per-branch Q-value instrumentation - Hold: min_hold_bars 5→1 Phase 2 — VarStore elimination: - Deleted copy_weights_from from all network types - Deleted to_varstore from DuelingQNetwork + DistributionalDuelingQNetwork - Deleted branching_to_varstore, 7 extract/sync VarStore functions - Deleted update_target_networks, legacy CPU training paths - Deleted iqn_target_network, target_network fields - Deleted gpu_kernel_parity_test.rs (705 lines) - Refactored GpuExperienceCollector to single flat buffer - Added weight_sets_from_branching() zero-copy replacement - EMA tau=1.0 init for target_params_buf KNOWN ISSUE: params_buf is zero-initialized but from_flat_buffer() creates weight set pointers into it BEFORE flatten_online_weights runs. The online forward reads zeros → NaN. Needs xavier_init_params_buf() at construction. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2345 lines
96 KiB
Rust
2345 lines
96 KiB
Rust
#![allow(
|
||
clippy::assertions_on_constants,
|
||
clippy::assertions_on_result_states,
|
||
clippy::clone_on_copy,
|
||
clippy::decimal_literal_representation,
|
||
clippy::doc_markdown,
|
||
clippy::empty_line_after_doc_comments,
|
||
clippy::field_reassign_with_default,
|
||
clippy::get_unwrap,
|
||
clippy::identity_op,
|
||
clippy::inconsistent_digit_grouping,
|
||
clippy::indexing_slicing,
|
||
clippy::integer_division,
|
||
clippy::len_zero,
|
||
clippy::let_underscore_must_use,
|
||
clippy::manual_div_ceil,
|
||
clippy::manual_let_else,
|
||
clippy::manual_range_contains,
|
||
clippy::modulo_arithmetic,
|
||
clippy::needless_range_loop,
|
||
clippy::non_ascii_literal,
|
||
clippy::redundant_clone,
|
||
clippy::shadow_reuse,
|
||
clippy::shadow_same,
|
||
clippy::shadow_unrelated,
|
||
clippy::single_match_else,
|
||
clippy::str_to_string,
|
||
clippy::string_slice,
|
||
clippy::tests_outside_test_module,
|
||
clippy::too_many_lines,
|
||
clippy::unnecessary_wraps,
|
||
clippy::unseparated_literal_suffix,
|
||
clippy::use_debug,
|
||
clippy::useless_vec,
|
||
clippy::wildcard_enum_match_arm,
|
||
clippy::else_if_without_else,
|
||
clippy::expect_used,
|
||
clippy::missing_const_for_fn,
|
||
clippy::similar_names,
|
||
clippy::type_complexity,
|
||
clippy::collapsible_else_if,
|
||
clippy::doc_lazy_continuation,
|
||
clippy::items_after_test_module,
|
||
clippy::map_clone,
|
||
clippy::multiple_unsafe_ops_per_block,
|
||
clippy::unwrap_or_default,
|
||
clippy::assign_op_pattern,
|
||
clippy::needless_borrow,
|
||
clippy::println_empty_string,
|
||
clippy::unnecessary_cast,
|
||
clippy::used_underscore_binding,
|
||
clippy::create_dir,
|
||
clippy::implicit_saturating_sub,
|
||
clippy::exit,
|
||
clippy::expect_fun_call,
|
||
clippy::too_many_arguments,
|
||
clippy::unnecessary_map_or,
|
||
clippy::unwrap_used,
|
||
dead_code,
|
||
unused_imports,
|
||
unused_variables,
|
||
clippy::cloned_ref_to_slice_refs,
|
||
clippy::neg_multiply,
|
||
clippy::while_let_loop,
|
||
clippy::bool_assert_comparison,
|
||
clippy::excessive_precision,
|
||
clippy::trivially_copy_pass_by_ref,
|
||
clippy::op_ref,
|
||
clippy::redundant_closure,
|
||
clippy::unnecessary_lazy_evaluations,
|
||
clippy::if_then_some_else_none,
|
||
clippy::unnecessary_to_owned,
|
||
clippy::single_component_path_imports,
|
||
)]
|
||
//! Walk-forward evaluation binary for DQN and PPO baseline models.
|
||
//!
|
||
//! Loads trained model checkpoints, runs inference on walk-forward test data,
|
||
//! computes financial metrics (Sharpe, drawdown, win rate, profit factor),
|
||
//! and generates a JSON report.
|
||
//!
|
||
//! # GPU-Batched Inference
|
||
//!
|
||
//! Both DQN and PPO evaluation use chunked GPU-batched inference (chunk size 1024).
|
||
//! Instead of one GPU forward pass per bar (N kernel launches), bars are grouped into
|
||
//! chunks with shared portfolio state, and each chunk does a single batched GPU forward
|
||
//! pass. This reduces GPU kernel launches by ~1000x vs the old per-bar loop.
|
||
//!
|
||
//! Within a chunk, all bars share the portfolio state (equity, exposure, spread) from
|
||
//! the start of the chunk. Between chunks, the portfolio state is updated based on the
|
||
//! sequential trade simulation results. This matches the hyperopt adapter approach.
|
||
//!
|
||
//! # Usage
|
||
//!
|
||
//! ```bash
|
||
//! SQLX_OFFLINE=true cargo run -p ml --example evaluate_baseline -- \
|
||
//! --model both --models-dir ml/trained_models \
|
||
//! --data-dir test_data/futures-baseline \
|
||
//! --output ml/trained_models/evaluation_report.json
|
||
//! ```
|
||
|
||
#![allow(unused_crate_dependencies)]
|
||
#![deny(
|
||
clippy::unwrap_used,
|
||
clippy::expect_used,
|
||
clippy::panic,
|
||
clippy::indexing_slicing
|
||
)]
|
||
|
||
use std::path::{Path, PathBuf};
|
||
|
||
use anyhow::{Context, Result};
|
||
use clap::Parser;
|
||
use serde::Serialize;
|
||
use serde_json::Value;
|
||
use tracing::{error, info, warn};
|
||
|
||
use common::metrics::{server as metrics_server, training_metrics as tm};
|
||
use ml::common::action::{ExposureLevel, FactoredAction, OrderType as ActionOrderType, Urgency};
|
||
use ml::dqn::{DQNConfig, OrderRouter, DQN};
|
||
use ml::features::extraction::FeatureVector;
|
||
|
||
#[allow(unreachable_pub)]
|
||
mod baseline_common;
|
||
use baseline_common::{load_all_bars, spread_cost_bps};
|
||
use ml::features::extraction::extract_ml_features;
|
||
use ml::ppo::ppo::{PPOConfig, PPO};
|
||
use ml::types::OHLCVBar;
|
||
use ml::walk_forward::{generate_walk_forward_windows, NormStats, WalkForwardConfig};
|
||
|
||
// Supervised model imports (same as evaluate_supervised)
|
||
use ml::training::unified_trainer::UnifiedTrainable;
|
||
use ml::diffusion::{DiffusionConfig, DiffusionTrainableAdapter};
|
||
use ml::kan::{KANConfig, KANTrainableAdapter};
|
||
use ml::liquid::{CfCTrainConfig, DeviceConfig, LiquidTrainableAdapter};
|
||
use ml::mamba::{Mamba2Config, trainable_adapter::Mamba2TrainableAdapter};
|
||
use ml::tft::{TFTConfig, TrainableTFT};
|
||
use ml::tgnn::trainable_adapter::TGGNTrainableAdapter;
|
||
use ml::tgnn::TGGNConfig;
|
||
use ml::tlob::{TLOBAdapterConfig, TLOBTrainableAdapter};
|
||
use ml::xlstm::{XLSTMConfig, XLSTMTrainableAdapter};
|
||
|
||
use ml_core::cuda_autograd::GpuTensor;
|
||
use std::sync::Arc;
|
||
use cudarc::driver::CudaStream;
|
||
|
||
|
||
/// Number of bars processed per GPU forward pass.
|
||
///
|
||
/// All bars in a chunk share the portfolio state (equity, exposure, spread) from
|
||
/// the start of the chunk. The portfolio is updated sequentially on CPU between
|
||
/// chunks. With 1024 bars, the stale-portfolio approximation is negligible
|
||
/// (~0.7 trading days for ES) while reducing GPU kernel launches by ~1000x.
|
||
const EVAL_CHUNK_SIZE: usize = 1024;
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// CLI Arguments
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Walk-forward evaluation binary for DQN/PPO baseline models.
|
||
#[derive(Parser, Debug)]
|
||
#[command(
|
||
name = "evaluate_baseline",
|
||
about = "Evaluate trained DQN/PPO checkpoints with walk-forward test data"
|
||
)]
|
||
struct Args {
|
||
/// Directory containing trained model checkpoints
|
||
#[arg(long, default_value = "ml/trained_models")]
|
||
models_dir: PathBuf,
|
||
|
||
/// Path to directory containing .dbn.zst files (env: FOXHUNT_DATA_DIR)
|
||
#[arg(long, env = "FOXHUNT_DATA_DIR")]
|
||
data_dir: PathBuf,
|
||
|
||
/// Output path for evaluation report JSON
|
||
#[arg(long, default_value = "ml/trained_models/evaluation_report.json")]
|
||
output: PathBuf,
|
||
|
||
/// Which model(s) to evaluate: "dqn", "ppo", or "both"
|
||
#[arg(long, default_value = "both")]
|
||
model: String,
|
||
|
||
/// Feature dimension (51 market + 3 portfolio = 54, must match training config)
|
||
#[arg(long, default_value_t = 54)]
|
||
feature_dim: usize,
|
||
|
||
/// Number of actions (5 exposure levels for DQN, pass --num-actions 45 for PPO)
|
||
#[arg(long, default_value_t = 5)]
|
||
num_actions: usize,
|
||
|
||
/// Walk-forward: initial training window in months
|
||
#[arg(long, default_value_t = 12)]
|
||
train_months: u32,
|
||
|
||
/// Walk-forward: validation window in months
|
||
#[arg(long, default_value_t = 3)]
|
||
val_months: u32,
|
||
|
||
/// Walk-forward: test window in months
|
||
#[arg(long, default_value_t = 3)]
|
||
test_months: u32,
|
||
|
||
/// Walk-forward: step size in months between folds
|
||
#[arg(long, default_value_t = 3)]
|
||
step_months: u32,
|
||
|
||
/// Symbol subdirectory to load (e.g. "ES.FUT", "NQ.FUT")
|
||
#[arg(long, default_value = "ES.FUT")]
|
||
symbol: String,
|
||
|
||
/// Maximum absolute per-bar return; larger moves are clamped (contract roll filter)
|
||
#[arg(long, default_value_t = 0.01)]
|
||
max_bar_return: f64,
|
||
|
||
/// Round-trip commission cost in basis points (1 bps = 0.01%)
|
||
/// Applied to BUY/SELL returns; HOLD is free.
|
||
/// Default 1.0 bps covers ~$4 exchange+broker for ES e-mini.
|
||
#[arg(long, default_value_t = 1.0)]
|
||
tx_cost_bps: f64,
|
||
|
||
/// Instrument tick size in price units (ES=0.25, NQ=0.25, ZN=1/64)
|
||
#[arg(long, default_value_t = 0.25)]
|
||
tick_size: f64,
|
||
|
||
/// Typical bid-ask spread in ticks (ES=1.0, ZN=1.0, 6E=2.0)
|
||
/// Full spread slippage is added to `tx_cost_bps` per round-trip trade.
|
||
#[arg(long, default_value_t = 1.0)]
|
||
spread_ticks: f64,
|
||
|
||
/// Bars per year for Sharpe annualization (ES/NQ=347760, 6E=345000, ZN=105840).
|
||
/// Default 347760 = 252 trading days × 1380 bars/day (ES 23h session).
|
||
#[arg(long, default_value_t = 347_760.0)]
|
||
bars_per_year: f64,
|
||
|
||
/// Optional path to hyperopt results JSON -- overrides matching config fields
|
||
/// (must match the file used during training so network architecture is identical)
|
||
#[arg(long)]
|
||
hyperopt_params: Option<PathBuf>,
|
||
|
||
/// Use GPU-accelerated backtest evaluation (default: enabled).
|
||
///
|
||
/// When enabled and CUDA is available, DQN folds are evaluated using
|
||
/// `GpuBacktestEvaluator` — all bars processed on GPU with a single metrics
|
||
/// readback. Results differ slightly from the CPU path because the GPU path
|
||
/// uses greedy argmax while the CPU path uses hierarchical softmax.
|
||
/// Falls back to the CPU path on any GPU error.
|
||
///
|
||
/// Pass `--no-gpu-eval` to force the CPU path.
|
||
#[arg(long, default_value_t = true)]
|
||
gpu_eval: bool,
|
||
|
||
/// Initial capital for GPU backtest evaluator (used only with --gpu-eval).
|
||
#[arg(long, default_value_t = 100_000.0)]
|
||
initial_capital: f64,
|
||
|
||
/// Maximum absolute position size in contracts (must match training config).
|
||
/// Disable leverage cap to match training env (no leverage constraint during training).
|
||
#[arg(long, default_value_t = 2.0)]
|
||
max_position: f64,
|
||
|
||
/// Use CUDA Graph capture for the DQN evaluation step loop.
|
||
///
|
||
/// When enabled, the entire step loop (gather + forward + env_step for
|
||
/// each step) is captured into a CUDA Graph on the first fold and
|
||
/// replayed for subsequent calls. Eliminates per-step kernel launch
|
||
/// overhead (~5-10us per launch × 4 kernels × max_len steps).
|
||
///
|
||
/// Falls back to the non-graphed GPU path if capture fails.
|
||
/// Only applies to the DQN pure-CUDA forward path (--gpu-eval).
|
||
#[arg(long, default_value_t = false)]
|
||
cuda_graphs: bool,
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Hyperopt parameter loading
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Load `best_params` from a hyperopt results JSON file.
|
||
///
|
||
/// Expected format: `{ "model_key": { "best_params": { ... }, ... } }`
|
||
/// Returns `None` if the file doesn't exist or can't be parsed.
|
||
#[allow(clippy::cognitive_complexity)]
|
||
fn load_hyperopt_params(hp_path: &Option<PathBuf>, model_key: &str) -> Option<Value> {
|
||
let file_path = hp_path.as_ref()?;
|
||
if !file_path.exists() {
|
||
info!("Hyperopt params file not found: {}, using defaults", file_path.display());
|
||
return None;
|
||
}
|
||
let contents = match std::fs::read_to_string(file_path) {
|
||
Ok(c) => c,
|
||
Err(e) => {
|
||
warn!("Failed to read hyperopt params {}: {}", file_path.display(), e);
|
||
return None;
|
||
}
|
||
};
|
||
let json: Value = match serde_json::from_str(&contents) {
|
||
Ok(v) => v,
|
||
Err(e) => {
|
||
warn!("Failed to parse hyperopt params JSON: {}", e);
|
||
return None;
|
||
}
|
||
};
|
||
let params = json.get(model_key)
|
||
.and_then(|m| m.get("best_params"))
|
||
.cloned();
|
||
if params.is_some() {
|
||
info!("Loaded hyperopt params for '{}' from {}", model_key, file_path.display());
|
||
} else {
|
||
warn!("No best_params found for '{}' in {}", model_key, file_path.display());
|
||
}
|
||
params
|
||
}
|
||
|
||
fn hp_f64(params: &Option<Value>, key: &str) -> Option<f64> {
|
||
params.as_ref()?.get(key)?.as_f64()
|
||
}
|
||
|
||
fn hp_usize(params: &Option<Value>, key: &str) -> Option<usize> {
|
||
params.as_ref()?.get(key)?.as_u64().map(|v| v as usize)
|
||
}
|
||
|
||
fn hp_bool(params: &Option<Value>, key: &str) -> Option<bool> {
|
||
params.as_ref()?.get(key)?.as_bool()
|
||
}
|
||
|
||
/// All 8 supervised model names recognised by this binary.
|
||
const SUPERVISED_MODEL_NAMES: &[&str] = &[
|
||
"tft", "mamba2", "liquid", "tggn", "tlob", "kan", "xlstm", "diffusion",
|
||
];
|
||
|
||
/// Create a supervised model with default architecture (same defaults as
|
||
/// `train_baseline_supervised` / `evaluate_supervised`). The model is
|
||
/// freshly initialised — call `load_checkpoint` afterwards to populate
|
||
/// trained weights.
|
||
fn create_supervised_model(
|
||
name: &str,
|
||
feature_dim: usize,
|
||
) -> Result<Box<dyn UnifiedTrainable>> {
|
||
let lr = 1e-3; // irrelevant for eval, but constructors require it
|
||
match name {
|
||
"tft" => {
|
||
let config = TFTConfig {
|
||
input_dim: feature_dim,
|
||
hidden_dim: 128,
|
||
num_heads: 4,
|
||
num_layers: 2,
|
||
num_quantiles: 3,
|
||
num_static_features: 0,
|
||
num_known_features: 0,
|
||
num_unknown_features: feature_dim,
|
||
sequence_length: 1,
|
||
prediction_horizon: 1,
|
||
dropout_rate: 0.1,
|
||
..TFTConfig::default()
|
||
};
|
||
let mut adapter = TrainableTFT::new(config)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create TFT: {}", e))?;
|
||
adapter
|
||
.set_learning_rate(lr)
|
||
.map_err(|e| anyhow::anyhow!("Failed to set TFT learning rate: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
"mamba2" => {
|
||
let config = Mamba2Config {
|
||
d_model: 128,
|
||
num_layers: 4,
|
||
d_state: 16,
|
||
max_seq_len: 60,
|
||
..Mamba2Config::default()
|
||
};
|
||
let native_dev = ml_core::native_types::NativeDevice::Cuda(0);
|
||
let mut adapter = Mamba2TrainableAdapter::from_native_device(config, &native_dev)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create Mamba2: {}", e))?;
|
||
adapter
|
||
.set_learning_rate(lr)
|
||
.map_err(|e| anyhow::anyhow!("Failed to set Mamba2 learning rate: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
"liquid" => {
|
||
let config = CfCTrainConfig {
|
||
input_size: feature_dim,
|
||
hidden_size: 128,
|
||
output_size: 1,
|
||
backbone_hidden_sizes: vec![128, 64],
|
||
learning_rate: lr,
|
||
device: DeviceConfig::Auto,
|
||
..CfCTrainConfig::default()
|
||
};
|
||
let adapter = LiquidTrainableAdapter::new(config)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create Liquid: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
"tggn" => {
|
||
let config = TGGNConfig {
|
||
node_dim: feature_dim,
|
||
hidden_dim: 32,
|
||
num_layers: 2,
|
||
max_nodes: 64,
|
||
max_edges: 128,
|
||
edge_dim: 4,
|
||
temporal_decay: 0.99,
|
||
update_frequency_ns: 1_000_000,
|
||
use_simd: false,
|
||
};
|
||
let mut adapter = TGGNTrainableAdapter::new(config)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create TGGN: {}", e))?;
|
||
adapter
|
||
.set_learning_rate(lr)
|
||
.map_err(|e| anyhow::anyhow!("Failed to set TGGN learning rate: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
"tlob" => {
|
||
let config = TLOBAdapterConfig {
|
||
d_model: 128,
|
||
num_heads: 4,
|
||
num_layers: 2,
|
||
seq_len: 1,
|
||
feature_dim,
|
||
};
|
||
let mut adapter = TLOBTrainableAdapter::new(config)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create TLOB: {}", e))?;
|
||
adapter
|
||
.set_learning_rate(lr)
|
||
.map_err(|e| anyhow::anyhow!("Failed to set TLOB learning rate: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
"kan" => {
|
||
let config = KANConfig {
|
||
grid_size: 5,
|
||
spline_order: 4,
|
||
layer_widths: vec![feature_dim, 32, 16, 1],
|
||
learning_rate: lr,
|
||
weight_decay: 1e-4,
|
||
grad_clip: 1.0,
|
||
};
|
||
let adapter = KANTrainableAdapter::new(config)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create KAN: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
"xlstm" => {
|
||
let config = XLSTMConfig {
|
||
input_dim: feature_dim,
|
||
hidden_dim: 128,
|
||
..XLSTMConfig::default()
|
||
};
|
||
let mut adapter = XLSTMTrainableAdapter::new(config)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create xLSTM: {}", e))?;
|
||
adapter
|
||
.set_learning_rate(lr)
|
||
.map_err(|e| anyhow::anyhow!("Failed to set xLSTM learning rate: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
"diffusion" => {
|
||
let config = DiffusionConfig {
|
||
feature_dim,
|
||
hidden_dim: 128,
|
||
..DiffusionConfig::default()
|
||
};
|
||
let ctx = cudarc::driver::CudaContext::new(0)
|
||
.map_err(|e| anyhow::anyhow!("CUDA context for Diffusion: {}", e))?;
|
||
let diff_stream = ctx.new_stream()
|
||
.map_err(|e| anyhow::anyhow!("CUDA stream for Diffusion: {}", e))?;
|
||
let adapter = DiffusionTrainableAdapter::new(config, &diff_stream)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create Diffusion: {}", e))?;
|
||
Ok(Box::new(adapter))
|
||
}
|
||
_ => anyhow::bail!("Unknown supervised model: {}", name),
|
||
}
|
||
}
|
||
|
||
/// Locate the best checkpoint for a supervised model fold.
|
||
///
|
||
/// Convention: `<models_dir>/<model>/<model>_fold<N>_best` (with `.json` metadata).
|
||
/// Falls back to `<models_dir>/<model>_fold<N>_best` for flat layouts.
|
||
fn find_supervised_checkpoint(
|
||
models_dir: &std::path::Path,
|
||
model_name: &str,
|
||
fold: usize,
|
||
) -> Result<std::path::PathBuf> {
|
||
let subdir_ckpt = models_dir
|
||
.join(model_name)
|
||
.join(format!("{}_fold{}_best", model_name, fold));
|
||
let meta_subdir = format!("{}.json", subdir_ckpt.display());
|
||
if std::path::Path::new(&meta_subdir).exists() {
|
||
return Ok(subdir_ckpt);
|
||
}
|
||
|
||
// Flat layout fallback
|
||
let flat_ckpt = models_dir.join(format!("{}_fold{}_best", model_name, fold));
|
||
let meta_flat = format!("{}.json", flat_ckpt.display());
|
||
if std::path::Path::new(&meta_flat).exists() {
|
||
return Ok(flat_ckpt);
|
||
}
|
||
|
||
anyhow::bail!(
|
||
"No {} checkpoint found for fold {} in {} (tried {} and {})",
|
||
model_name,
|
||
fold,
|
||
models_dir.display(),
|
||
subdir_ckpt.display(),
|
||
flat_ckpt.display(),
|
||
)
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Report Data Types
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Metrics for a single fold/model combination.
|
||
#[derive(Debug, Serialize)]
|
||
struct FoldMetrics {
|
||
fold: usize,
|
||
model: String,
|
||
sharpe_ratio: f64,
|
||
/// Trade-level Sharpe (non-zero returns only, sqrt(252) annualization).
|
||
/// Comparable to hyperopt's `PerformanceMetrics::from_trades` Sharpe.
|
||
trade_sharpe_ratio: f64,
|
||
max_drawdown_pct: f64,
|
||
win_rate_pct: f64,
|
||
profit_factor: f64,
|
||
total_return_pct: f64,
|
||
num_trades: usize,
|
||
test_start: String,
|
||
test_end: String,
|
||
}
|
||
|
||
/// Aggregate metrics across all folds for both models.
|
||
#[derive(Debug, Serialize)]
|
||
struct AggregateMetrics {
|
||
dqn_avg_sharpe: f64,
|
||
dqn_avg_trade_sharpe: f64,
|
||
dqn_avg_drawdown: f64,
|
||
dqn_avg_win_rate: f64,
|
||
ppo_avg_sharpe: f64,
|
||
ppo_avg_trade_sharpe: f64,
|
||
ppo_avg_drawdown: f64,
|
||
ppo_avg_win_rate: f64,
|
||
}
|
||
|
||
/// Sanity checks to flag obviously broken models.
|
||
#[derive(Debug, Serialize)]
|
||
struct SanityChecks {
|
||
/// True if any model has Sharpe > 0
|
||
beats_random: bool,
|
||
/// True if all 3 actions (buy/sell/hold) were used
|
||
action_diversity: bool,
|
||
/// True if Sharpe std < 2x |mean Sharpe|
|
||
fold_consistency: bool,
|
||
}
|
||
|
||
/// Full evaluation report written to JSON.
|
||
#[derive(Debug, Serialize)]
|
||
struct EvaluationReport {
|
||
folds: Vec<FoldMetrics>,
|
||
aggregate: AggregateMetrics,
|
||
sanity_checks: SanityChecks,
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Financial Metrics
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Container for computed financial metrics from a sequence of trade returns.
|
||
struct ComputedMetrics {
|
||
sharpe_ratio: f64,
|
||
/// Trade-level Sharpe ratio (matches hyperopt's `PerformanceMetrics::from_trades`).
|
||
/// Computed from non-zero returns only, annualized with `sqrt(252)`.
|
||
trade_sharpe_ratio: f64,
|
||
max_drawdown_pct: f64,
|
||
win_rate_pct: f64,
|
||
profit_factor: f64,
|
||
total_return_pct: f64,
|
||
num_trades: usize,
|
||
}
|
||
|
||
/// Compute financial metrics from a sequence of per-bar trade returns.
|
||
///
|
||
/// - Sharpe (bar): annualized (mean / std * `sqrt(bars_per_year)`), includes HOLD bars
|
||
/// - Sharpe (trade): annualized (mean / std * `sqrt(252)`), non-zero returns only
|
||
/// - Max drawdown: largest peak-to-trough drop on cumulative equity curve (%)
|
||
/// - Win rate: percentage of returns > 0
|
||
/// - Profit factor: `gross_profit` / `gross_loss` (inf if no losses)
|
||
/// - Total return: sum of returns * 100 (as percentage)
|
||
/// - Num trades: count of non-zero returns (BUY or SELL actions)
|
||
fn compute_metrics(returns: &[f64], bars_per_year: f64) -> ComputedMetrics {
|
||
let n = returns.len();
|
||
if n == 0 {
|
||
return ComputedMetrics {
|
||
sharpe_ratio: 0.0,
|
||
trade_sharpe_ratio: 0.0,
|
||
max_drawdown_pct: 0.0,
|
||
win_rate_pct: 0.0,
|
||
profit_factor: 0.0,
|
||
total_return_pct: 0.0,
|
||
num_trades: 0,
|
||
};
|
||
}
|
||
|
||
// Count actual trades (non-zero returns, i.e. BUY or SELL actions)
|
||
let num_trades = returns.iter().filter(|&&r| r.abs() > 1e-12).count();
|
||
|
||
// Per-bar Sharpe: mean and std over ALL bars (including HOLD=0), annualized
|
||
// with sqrt(bars_per_year). This produces larger absolute values because the
|
||
// annualization factor (~590) is much larger than sqrt(252) (~15.9).
|
||
let sum: f64 = returns.iter().sum();
|
||
let mean = sum / n as f64;
|
||
|
||
let variance: f64 = returns.iter().map(|&r| (r - mean).powi(2)).sum::<f64>() / n as f64;
|
||
let std = variance.sqrt();
|
||
let sharpe_ratio = if std > 1e-12 {
|
||
(mean / std) * bars_per_year.sqrt()
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// Trade-level Sharpe (matches hyperopt's PerformanceMetrics::from_trades /
|
||
// calculate_sharpe_ratio in evaluation/metrics.rs):
|
||
// Only considers non-zero returns (actual trades), annualized with sqrt(252).
|
||
let trade_returns: Vec<f64> = returns.iter().copied().filter(|&r| r.abs() > 1e-12).collect();
|
||
let trade_sharpe_ratio = if trade_returns.len() > 1 {
|
||
let trade_mean: f64 = trade_returns.iter().sum::<f64>() / trade_returns.len() as f64;
|
||
let trade_var: f64 = trade_returns
|
||
.iter()
|
||
.map(|&r| (r - trade_mean).powi(2))
|
||
.sum::<f64>()
|
||
/ trade_returns.len() as f64;
|
||
let trade_std = trade_var.sqrt();
|
||
if trade_std > 1e-12 {
|
||
(trade_mean / trade_std) * 252.0_f64.sqrt()
|
||
} else {
|
||
0.0
|
||
}
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// Max drawdown on cumulative equity curve
|
||
let mut equity = 1.0_f64;
|
||
let mut peak = 1.0_f64;
|
||
let mut max_drawdown = 0.0_f64;
|
||
|
||
for &ret in returns {
|
||
equity *= 1.0 + ret;
|
||
if equity > peak {
|
||
peak = equity;
|
||
}
|
||
let drawdown = if peak > 1e-12 {
|
||
(peak - equity) / peak
|
||
} else {
|
||
0.0
|
||
};
|
||
if drawdown > max_drawdown {
|
||
max_drawdown = drawdown;
|
||
}
|
||
}
|
||
let max_drawdown_pct = max_drawdown * 100.0;
|
||
|
||
// Win rate
|
||
let wins = returns.iter().filter(|&&r| r > 0.0).count();
|
||
let win_rate_pct = if num_trades > 0 {
|
||
(wins as f64 / num_trades as f64) * 100.0
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// Profit factor
|
||
let gross_profit: f64 = returns.iter().filter(|&&r| r > 0.0).sum();
|
||
let gross_loss: f64 = returns.iter().filter(|&&r| r < 0.0).map(|&r| r.abs()).sum();
|
||
let profit_factor = if gross_loss > 1e-12 {
|
||
gross_profit / gross_loss
|
||
} else if gross_profit > 0.0 {
|
||
f64::INFINITY
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// Total return (from compounded equity curve)
|
||
let total_return_pct = (equity - 1.0) * 100.0;
|
||
|
||
ComputedMetrics {
|
||
sharpe_ratio,
|
||
trade_sharpe_ratio,
|
||
max_drawdown_pct,
|
||
win_rate_pct,
|
||
profit_factor,
|
||
total_return_pct,
|
||
num_trades,
|
||
}
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// GPU-Batched Inference Helpers
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Mutable portfolio state carried across chunks during evaluation.
|
||
struct PortfolioState {
|
||
equity: f64,
|
||
current_exposure: f64,
|
||
}
|
||
|
||
/// Build flat f32 state vectors for a chunk of bars.
|
||
///
|
||
/// Portfolio features (equity, exposure, spread) are frozen at the values from
|
||
/// the START of the chunk. Market features are per-bar. Returns a contiguous
|
||
/// `Vec<f32>` of length `chunk_len * feature_dim`.
|
||
fn build_chunk_states(
|
||
test_features: &[FeatureVector],
|
||
test_bars: &[OHLCVBar],
|
||
chunk_start: usize,
|
||
chunk_end: usize,
|
||
feature_dim: usize,
|
||
portfolio: &PortfolioState,
|
||
tick_size: f64,
|
||
spread_ticks: f64,
|
||
) -> Vec<f32> {
|
||
let chunk_len = chunk_end - chunk_start;
|
||
let chunk_equity = portfolio.equity as f32;
|
||
let chunk_exposure = portfolio.current_exposure as f32;
|
||
let mut flat_states: Vec<f32> = Vec::with_capacity(chunk_len * feature_dim);
|
||
|
||
for bar_idx in chunk_start..chunk_end {
|
||
let Some(feat) = test_features.get(bar_idx) else {
|
||
flat_states.extend(std::iter::repeat_n(0.0_f32, feature_dim));
|
||
continue;
|
||
};
|
||
|
||
// 51 market features (f64 -> f32)
|
||
for &v in feat {
|
||
flat_states.push(v as f32);
|
||
}
|
||
|
||
// 3 portfolio features matching training's PortfolioTracker.get_portfolio_features():
|
||
// [0] normalized_value = portfolio_value / initial_capital (~1.0)
|
||
// [1] normalized_position = position_size / max_position (-1.0 to +1.0)
|
||
// [2] avg_spread = tick_size * spread_ticks / price (~0.0001)
|
||
let close = test_bars.get(bar_idx).map(|b| b.close).unwrap_or(1.0);
|
||
let spread_estimate = if close > 1e-12 {
|
||
(tick_size * spread_ticks / close) as f32
|
||
} else {
|
||
0.0001_f32
|
||
};
|
||
flat_states.push(chunk_equity); // normalized_value
|
||
flat_states.push(chunk_exposure); // normalized_position
|
||
flat_states.push(spread_estimate); // avg_spread
|
||
}
|
||
|
||
flat_states
|
||
}
|
||
|
||
/// Simulate trades for a chunk of action indices, updating portfolio state and
|
||
/// accumulating returns and action counts.
|
||
///
|
||
/// Processes action indices sequentially on CPU. Each action maps to a
|
||
/// `FactoredAction` with exposure, order type, and urgency. Transaction costs
|
||
/// are proportional to position delta and differentiated by order type/urgency.
|
||
fn simulate_chunk_trades(
|
||
action_indices: &[usize],
|
||
chunk_start: usize,
|
||
test_bars: &[OHLCVBar],
|
||
args: &Args,
|
||
portfolio: &mut PortfolioState,
|
||
returns: &mut Vec<f64>,
|
||
action_counts: &mut [usize; 3],
|
||
model_name: &str,
|
||
is_dqn: bool,
|
||
) {
|
||
for (i, &action_idx) in action_indices.iter().enumerate() {
|
||
let bar_idx = chunk_start + i;
|
||
|
||
let action = if is_dqn {
|
||
match ExposureLevel::from_index(action_idx) {
|
||
Ok(exposure) => OrderRouter::route_default(exposure),
|
||
Err(e) => {
|
||
warn!(" [{}] exposure_from_index({}) error at bar {}: {}", model_name, action_idx, bar_idx, e);
|
||
FactoredAction::new(ExposureLevel::Flat, ActionOrderType::Market, Urgency::Normal)
|
||
}
|
||
}
|
||
} else {
|
||
match FactoredAction::from_index(action_idx) {
|
||
Ok(fa) => fa,
|
||
Err(e) => {
|
||
warn!(" [{}] from_index({}) error at bar {}: {}", model_name, action_idx, bar_idx, e);
|
||
FactoredAction::new(ExposureLevel::Flat, ActionOrderType::Market, Urgency::Normal)
|
||
}
|
||
}
|
||
};
|
||
|
||
// Track action diversity by legacy category (buy/sell/hold)
|
||
let legacy_idx = if action.is_buy() { 0 } else if action.is_sell() { 1 } else { 2 };
|
||
if let Some(count) = action_counts.get_mut(legacy_idx) {
|
||
*count += 1;
|
||
}
|
||
|
||
// Compute percentage return, clamped to filter contract roll boundaries
|
||
let close_cur = test_bars.get(bar_idx).map(|b| b.close).unwrap_or(0.0);
|
||
let close_next = test_bars.get(bar_idx + 1).map(|b| b.close).unwrap_or(close_cur);
|
||
let pct_change = if close_cur.abs() > 1e-12 {
|
||
((close_next - close_cur) / close_cur).clamp(-args.max_bar_return, args.max_bar_return)
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// Position delta: only incur costs when position actually changes
|
||
let target_exposure = action.target_exposure();
|
||
let position_delta = (target_exposure - portfolio.current_exposure).abs();
|
||
|
||
// Order-type and urgency-differentiated transaction cost:
|
||
// - Market orders: 15 bps, LimitMaker: 5 bps, IoC: 10 bps
|
||
// - Urgency scales cost: Patient=0.5x, Normal=1.0x, Aggressive=1.5x
|
||
// Cost is proportional to position delta (no cost for holding same position)
|
||
let tx_cost = if position_delta > 1e-12 {
|
||
let base_bps = args.tx_cost_bps + spread_cost_bps(close_cur, args.tick_size, args.spread_ticks);
|
||
let order_cost_frac = action.transaction_cost(); // 0.0015/0.0005/0.0010
|
||
let urgency_mult = action.urgency_weight(); // 0.5/1.0/1.5
|
||
position_delta * (base_bps * 0.0001 + order_cost_frac * urgency_mult)
|
||
} else {
|
||
0.0
|
||
};
|
||
|
||
// Exposure-weighted return minus differentiated transaction cost
|
||
let ret = target_exposure * pct_change - tx_cost;
|
||
returns.push(ret);
|
||
|
||
// Update portfolio state for next bar (and next chunk's frozen state)
|
||
portfolio.equity *= 1.0 + ret;
|
||
portfolio.current_exposure = target_exposure;
|
||
}
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// DQN Evaluation
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Run DQN inference on test features using GPU-batched forward passes and return
|
||
/// per-bar trade returns and action counts (buy, sell, hold).
|
||
///
|
||
/// Bars are processed in chunks of `EVAL_CHUNK_SIZE` (1024). Each chunk:
|
||
/// 1. Builds state vectors on CPU with current portfolio features (equity, exposure, spread)
|
||
/// 2. Transfers the `[chunk_len, 54]` tensor to GPU in a single copy
|
||
/// 3. Executes one batched GPU forward pass via `batch_greedy_actions` (argmax of Q-values)
|
||
/// 4. Transfers only the action index vector (`Vec<usize>`) back to CPU
|
||
/// 5. Simulates trades sequentially on CPU to update portfolio state for the next chunk
|
||
///
|
||
/// This reduces GPU kernel launches from N (one per bar) to ceil(N/1024).
|
||
#[allow(clippy::cognitive_complexity)]
|
||
fn evaluate_dqn_fold(
|
||
fold: usize,
|
||
test_features: &[FeatureVector],
|
||
test_bars: &[OHLCVBar],
|
||
models_dir: &Path,
|
||
args: &Args,
|
||
hp: &Option<Value>,
|
||
) -> Result<(Vec<f64>, [usize; 3])> {
|
||
// Prefer `_best` checkpoint; fall back to highest `_epoch{N}` if training
|
||
// early-stopped without marking a "best" (e.g. validation loss plateaued).
|
||
let best_path = models_dir.join(format!("dqn_fold{}_best.safetensors", fold));
|
||
let ckpt_path = if best_path.exists() {
|
||
best_path
|
||
} else {
|
||
// Glob for epoch checkpoints and pick the highest epoch number
|
||
let pattern = format!("dqn_fold{}_epoch", fold);
|
||
let mut candidates: Vec<_> = std::fs::read_dir(models_dir)
|
||
.ok()
|
||
.into_iter()
|
||
.flatten()
|
||
.filter_map(|e| e.ok())
|
||
.filter(|e| {
|
||
let name = e.file_name();
|
||
let s = name.to_string_lossy();
|
||
s.starts_with(&pattern) && s.ends_with(".safetensors")
|
||
})
|
||
.collect();
|
||
candidates.sort_by_key(|e| std::cmp::Reverse(e.file_name()));
|
||
match candidates.first() {
|
||
Some(entry) => {
|
||
let p = entry.path();
|
||
info!("DQN fold {} using fallback checkpoint: {}", fold, p.display());
|
||
p
|
||
}
|
||
None => {
|
||
anyhow::bail!(
|
||
"No DQN checkpoint found for fold {} in {}",
|
||
fold, models_dir.display()
|
||
);
|
||
}
|
||
}
|
||
};
|
||
|
||
// Create DQN with same config as training — all architecture-affecting params
|
||
// must match exactly, otherwise checkpoint loading fails (tensor shape mismatch).
|
||
#[allow(clippy::integer_division)]
|
||
let config = DQNConfig {
|
||
state_dim: args.feature_dim,
|
||
num_actions: args.num_actions,
|
||
hidden_dims: {
|
||
let base = hp_usize(hp, "hidden_dim_base").unwrap_or(256);
|
||
// Align to 8 for tensor cores (matches training)
|
||
let align = |x: usize| -> usize { x.div_ceil(8) * 8 };
|
||
vec![align(base), align(base / 2), align(base / 4)]
|
||
},
|
||
learning_rate: 1e-4,
|
||
gamma: hp_f64(hp, "gamma").unwrap_or(0.95) as f32,
|
||
epsilon_start: 0.0, // No exploration during evaluation
|
||
epsilon_end: 0.0,
|
||
epsilon_decay: 1.0,
|
||
replay_buffer_capacity: 100, // Minimal buffer, not used for eval
|
||
batch_size: 64,
|
||
min_replay_size: 64,
|
||
target_update_freq: 500,
|
||
warmup_steps: 0,
|
||
// Architecture params — must match training checkpoint shapes
|
||
dueling_hidden_dim: hp_usize(hp, "dueling_hidden_dim").unwrap_or(128),
|
||
num_atoms: hp_usize(hp, "num_atoms").unwrap_or(51),
|
||
v_min: hp_f64(hp, "v_min").unwrap_or_else(|| {
|
||
let gamma = hp_f64(hp, "gamma").unwrap_or(0.95);
|
||
-(10.0 / (1.0 - gamma) * 1.2).clamp(20.0, 300.0)
|
||
}) as f32,
|
||
v_max: hp_f64(hp, "v_max").unwrap_or_else(|| {
|
||
let gamma = hp_f64(hp, "gamma").unwrap_or(0.95);
|
||
(10.0 / (1.0 - gamma) * 1.2).clamp(20.0, 300.0)
|
||
}) as f32,
|
||
iqn_num_quantiles: hp_usize(hp, "num_quantiles").unwrap_or(64),
|
||
..DQNConfig::default()
|
||
};
|
||
|
||
let mut dqn = DQN::new(config).context("Failed to create DQN model")?;
|
||
|
||
// Load trained weights (DQN::new auto-selects CUDA if available)
|
||
dqn.load_from_safetensors(&ckpt_path.to_string_lossy())
|
||
.with_context(|| format!("Failed to load DQN checkpoint: {}", ckpt_path.display()))?;
|
||
|
||
// B1 FIX: Set eval mode — disable noisy layer noise, use mean weights only.
|
||
// This matches hyperopt's action selection (no training noise during inference).
|
||
dqn.set_eval_mode(true)
|
||
.with_context(|| "Failed to set DQN eval mode")?;
|
||
|
||
let stream = dqn.cuda_stream().clone();
|
||
let eval_softmax_temp = hp_f64(hp, "eval_softmax_temp").unwrap_or(1.0);
|
||
info!(
|
||
" [DQN] Loaded checkpoint: {} (softmax_temp={:.2})",
|
||
ckpt_path.display(),
|
||
eval_softmax_temp,
|
||
);
|
||
|
||
// ── Per-bar inference with portfolio state sync ─────────────────────────
|
||
//
|
||
// B3 FIX: Process each bar individually to update portfolio features
|
||
// (equity, exposure) between bars. The old chunked approach froze
|
||
// portfolio state within each 1024-bar chunk, creating state mismatch.
|
||
//
|
||
// B1 FIX: Use hierarchical softmax action selection (matching hyperopt)
|
||
// instead of greedy argmax. This ensures eval results match training.
|
||
//
|
||
// Performance: ~N GPU calls instead of N/1024, but eval isn't latency-critical.
|
||
|
||
let eval_bars = test_features.len().saturating_sub(1); // last bar has no next-bar return
|
||
let feature_dim = args.feature_dim;
|
||
|
||
let mut returns = Vec::with_capacity(eval_bars);
|
||
let mut action_counts = [0_usize; 3]; // [buy, sell, hold]
|
||
let mut portfolio = PortfolioState { equity: 1.0, current_exposure: 0.0 };
|
||
|
||
info!(
|
||
" [DQN] Per-bar eval with portfolio sync: {} bars",
|
||
eval_bars,
|
||
);
|
||
|
||
for bar_idx in 0..eval_bars {
|
||
// Build single state vector with current portfolio features
|
||
let flat_state = build_chunk_states(
|
||
test_features, test_bars, bar_idx, bar_idx + 1,
|
||
feature_dim, &portfolio, args.tick_size, args.spread_ticks,
|
||
);
|
||
|
||
// Single-bar GPU forward pass + hierarchical softmax selection (B1)
|
||
let state_tensor = GpuTensor::from_host(&flat_state, vec![1, feature_dim], &stream)
|
||
.map_err(|e| anyhow::anyhow!("Failed to create DQN state tensor for bar {}: {}", bar_idx, e))?;
|
||
|
||
let action_tensor = dqn.batch_hierarchical_softmax_actions(&state_tensor, eval_softmax_temp)
|
||
.with_context(|| format!("DQN softmax action selection failed for bar {}", bar_idx))?;
|
||
let action_host = action_tensor.to_host(&stream)
|
||
.map_err(|e| anyhow::anyhow!("Action extraction: {e}"))?;
|
||
let action_idx = action_host.first().copied().unwrap_or(0.0) as usize;
|
||
let action_indices = vec![action_idx];
|
||
|
||
// Simulate trade and update portfolio state (B3: state synced per bar)
|
||
simulate_chunk_trades(
|
||
&action_indices, bar_idx, test_bars, args,
|
||
&mut portfolio, &mut returns, &mut action_counts, "DQN", true,
|
||
);
|
||
}
|
||
|
||
Ok((returns, action_counts))
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// GPU-Accelerated DQN Evaluation
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// GPU-accelerated DQN evaluation using `GpuBacktestEvaluator`.
|
||
///
|
||
/// Uses greedy argmax for action selection (not hierarchical softmax as in the
|
||
/// CPU path). Results will differ slightly from `evaluate_dqn_fold`. This is
|
||
/// intentional: the GPU path prioritises throughput (all bars processed in a
|
||
/// single GPU loop with one metrics readback) over exact matching of the CPU
|
||
/// softmax-based selection.
|
||
///
|
||
/// The function returns `Vec<WindowMetrics>` — one entry per window (here,
|
||
/// one window = the full test fold). Call sites are responsible for converting
|
||
/// these into `FoldMetrics` for the report.
|
||
|
||
#[allow(clippy::cognitive_complexity)]
|
||
fn evaluate_dqn_fold_gpu(
|
||
fold: usize,
|
||
test_features: &[ml::features::extraction::FeatureVector],
|
||
test_bars: &[ml::types::OHLCVBar],
|
||
models_dir: &std::path::Path,
|
||
args: &Args,
|
||
hp: &Option<serde_json::Value>,
|
||
) -> Result<Vec<ml::cuda_pipeline::gpu_backtest_evaluator::WindowMetrics>> {
|
||
use ml::cuda_pipeline::gpu_backtest_evaluator::{GpuBacktestConfig, GpuBacktestEvaluator};
|
||
|
||
// ── Load checkpoint (identical to CPU path) ───────────────────────────
|
||
let best_path = models_dir.join(format!("dqn_fold{}_best.safetensors", fold));
|
||
let ckpt_path = if best_path.exists() {
|
||
best_path
|
||
} else {
|
||
let pattern = format!("dqn_fold{}_epoch", fold);
|
||
let mut candidates: Vec<_> = std::fs::read_dir(models_dir)
|
||
.ok()
|
||
.into_iter()
|
||
.flatten()
|
||
.filter_map(|e| e.ok())
|
||
.filter(|e| {
|
||
let name = e.file_name();
|
||
let s = name.to_string_lossy();
|
||
s.starts_with(&pattern) && s.ends_with(".safetensors")
|
||
})
|
||
.collect();
|
||
candidates.sort_by_key(|e| std::cmp::Reverse(e.file_name()));
|
||
match candidates.first() {
|
||
Some(entry) => {
|
||
let p = entry.path();
|
||
info!(
|
||
" [DQN GPU] fold {} using fallback checkpoint: {}",
|
||
fold,
|
||
p.display()
|
||
);
|
||
p
|
||
}
|
||
None => {
|
||
anyhow::bail!(
|
||
"No DQN checkpoint found for fold {} in {}",
|
||
fold,
|
||
models_dir.display()
|
||
);
|
||
}
|
||
}
|
||
};
|
||
|
||
#[allow(clippy::integer_division)]
|
||
let config = DQNConfig {
|
||
state_dim: args.feature_dim,
|
||
num_actions: args.num_actions,
|
||
hidden_dims: {
|
||
let base = hp_usize(hp, "hidden_dim_base").unwrap_or(256);
|
||
let align = |x: usize| -> usize { x.div_ceil(8) * 8 };
|
||
vec![align(base), align(base / 2), align(base / 4)]
|
||
},
|
||
learning_rate: 1e-4,
|
||
gamma: hp_f64(hp, "gamma").unwrap_or(0.95) as f32,
|
||
epsilon_start: 0.0,
|
||
epsilon_end: 0.0,
|
||
epsilon_decay: 1.0,
|
||
replay_buffer_capacity: 100,
|
||
batch_size: 64,
|
||
min_replay_size: 64,
|
||
target_update_freq: 500,
|
||
warmup_steps: 0,
|
||
dueling_hidden_dim: hp_usize(hp, "dueling_hidden_dim").unwrap_or(128),
|
||
num_atoms: hp_usize(hp, "num_atoms").unwrap_or(51),
|
||
v_min: hp_f64(hp, "v_min").unwrap_or_else(|| {
|
||
let gamma = hp_f64(hp, "gamma").unwrap_or(0.95);
|
||
-(10.0 / (1.0 - gamma) * 1.2).clamp(20.0, 300.0)
|
||
}) as f32,
|
||
v_max: hp_f64(hp, "v_max").unwrap_or_else(|| {
|
||
let gamma = hp_f64(hp, "gamma").unwrap_or(0.95);
|
||
(10.0 / (1.0 - gamma) * 1.2).clamp(20.0, 300.0)
|
||
}) as f32,
|
||
iqn_num_quantiles: hp_usize(hp, "num_quantiles").unwrap_or(64),
|
||
..DQNConfig::default()
|
||
};
|
||
|
||
let mut dqn = DQN::new(config).context("Failed to create DQN model (GPU path)")?;
|
||
dqn.load_from_safetensors(&ckpt_path.to_string_lossy())
|
||
.with_context(|| {
|
||
format!(
|
||
"Failed to load DQN checkpoint (GPU path): {}",
|
||
ckpt_path.display()
|
||
)
|
||
})?;
|
||
dqn.set_eval_mode(true)
|
||
.context("Failed to set DQN eval mode (GPU path)")?;
|
||
|
||
let stream = dqn.cuda_stream().clone();
|
||
info!(
|
||
" [DQN GPU] Loaded checkpoint: {} (greedy argmax)",
|
||
ckpt_path.display(),
|
||
);
|
||
warn!(
|
||
" [DQN GPU] Spread cost uses constant tick_size*spread_ticks={:.6}, \
|
||
CPU path uses per-bar estimate — results will differ slightly",
|
||
args.tick_size * args.spread_ticks,
|
||
);
|
||
|
||
// ── Build single window from all test data ────────────────────────────
|
||
//
|
||
// The GpuBacktestEvaluator's `feature_dim` parameter is the number of
|
||
// MARKET features only (the gather kernel appends the 3 portfolio features
|
||
// internally). Total state_dim = feature_dim + 3.
|
||
let eval_bars = test_features.len().saturating_sub(1);
|
||
if eval_bars == 0 {
|
||
anyhow::bail!("No bars to evaluate in GPU path for fold {}", fold);
|
||
}
|
||
|
||
// Market features only (42 dims from extract_ml_features). The gather kernel
|
||
// appends live portfolio at [feat_dim..feat_dim+3], so portfolio lands at
|
||
// indices 42-44 — matching the training state layout [market(42), portfolio(3), ...].
|
||
let market_feature_dim: usize = 42;
|
||
|
||
let mut prices: Vec<[f32; 4]> = Vec::with_capacity(eval_bars);
|
||
let mut features: Vec<Vec<f32>> = Vec::with_capacity(eval_bars);
|
||
|
||
for bar_idx in 0..eval_bars {
|
||
let bar = test_bars.get(bar_idx).ok_or_else(|| {
|
||
anyhow::anyhow!("test_bars index {} out of bounds (len={})", bar_idx, test_bars.len())
|
||
})?;
|
||
let open = bar.open as f32;
|
||
let high = bar.high as f32;
|
||
let low = bar.low as f32;
|
||
let close = bar.close as f32;
|
||
prices.push([open, high, low, close]);
|
||
|
||
let fv = test_features.get(bar_idx).ok_or_else(|| {
|
||
anyhow::anyhow!(
|
||
"test_features index {} out of bounds (len={})",
|
||
bar_idx,
|
||
test_features.len()
|
||
)
|
||
})?;
|
||
// Take up to market_feature_dim values; truncate portfolio dims if any
|
||
let market_fv: Vec<f32> = fv
|
||
.iter()
|
||
.take(market_feature_dim)
|
||
.map(|&v| v as f32)
|
||
.collect();
|
||
features.push(market_fv);
|
||
}
|
||
|
||
let gpu_config = GpuBacktestConfig {
|
||
max_position: args.max_position as f32,
|
||
tx_cost_bps: args.tx_cost_bps as f32,
|
||
spread_cost: (args.tick_size * args.spread_ticks) as f32,
|
||
initial_capital: args.initial_capital as f32,
|
||
max_leverage: 0.0, // Disabled: match training env (no leverage cap)
|
||
..Default::default()
|
||
};
|
||
|
||
// Single window = full test fold
|
||
let mut evaluator = GpuBacktestEvaluator::new(
|
||
&[prices],
|
||
&[features],
|
||
market_feature_dim,
|
||
gpu_config,
|
||
&stream,
|
||
)
|
||
.with_context(|| format!("GpuBacktestEvaluator::new failed for fold {}", fold))?;
|
||
|
||
// ── Extract weights for pure-CUDA forward pass ─────────────────────
|
||
//
|
||
// Branching DQN is always active. Extract dueling + branching weight sets
|
||
// directly from the BranchingDuelingQNetwork (zero-copy pointer views).
|
||
// Fall back to the closure-based evaluate() path if no network is present.
|
||
let metrics = if let Some(ref br) = dqn.branching_q_network {
|
||
use ml::cuda_pipeline::gpu_weights::weight_sets_from_branching;
|
||
|
||
let cfg = br.config();
|
||
let sh1 = *cfg.shared_hidden_dims.first().ok_or_else(|| {
|
||
anyhow::anyhow!("Branching network has no shared hidden dims")
|
||
})?;
|
||
let sh2 = *cfg.shared_hidden_dims.get(1).ok_or_else(|| {
|
||
anyhow::anyhow!("Branching network needs at least 2 shared hidden dims")
|
||
})?;
|
||
let network_dims = (
|
||
sh1,
|
||
sh2,
|
||
cfg.value_hidden_dim,
|
||
cfg.branch_hidden_dim,
|
||
);
|
||
|
||
let (weights, branching_weights) = weight_sets_from_branching(br);
|
||
|
||
let dqn_cfg = ml::cuda_pipeline::gpu_backtest_evaluator::DqnBacktestConfig::from_network_dims(network_dims);
|
||
|
||
if args.cuda_graphs {
|
||
info!(
|
||
" [DQN GPU] Using CUDA Graph-captured forward pass (fold {}, dims=({},{},{},{}))",
|
||
fold, network_dims.0, network_dims.1, network_dims.2, network_dims.3,
|
||
);
|
||
evaluator
|
||
.evaluate_dqn_graphed(&weights, Some(&branching_weights), &dqn_cfg)
|
||
.with_context(|| format!(
|
||
"GpuBacktestEvaluator::evaluate_dqn_graphed failed for fold {}", fold
|
||
))?
|
||
} else {
|
||
info!(
|
||
" [DQN GPU] Using pure-CUDA forward pass (fold {}, dims=({},{},{},{}))",
|
||
fold, network_dims.0, network_dims.1, network_dims.2, network_dims.3,
|
||
);
|
||
evaluator
|
||
.evaluate_dqn(&weights, Some(&branching_weights), &dqn_cfg)
|
||
.with_context(|| format!(
|
||
"GpuBacktestEvaluator::evaluate_dqn failed for fold {}", fold
|
||
))?
|
||
}
|
||
} else {
|
||
// Non-branching fallback: use closure-based GPU forward pass
|
||
info!(
|
||
" [DQN GPU] No branching network, using closure forward path (fold {})",
|
||
fold
|
||
);
|
||
let eval_stream = stream.clone();
|
||
evaluator
|
||
.evaluate(
|
||
&|states_flat: &cudarc::driver::CudaSlice<f32>, batch_size: usize, state_dim: usize| -> Result<cudarc::driver::CudaSlice<i32>, ml::MLError> {
|
||
// Download f32 states to host, then upload as f32 GpuTensor for DQN forward
|
||
let host_f32 = eval_stream.clone_dtoh(states_flat)
|
||
.map_err(|e| ml::MLError::ModelError(format!("DtoH states: {e}")))?;
|
||
let states_tensor = GpuTensor::from_host(&host_f32, vec![batch_size, state_dim], &eval_stream)
|
||
.map_err(|e| ml::MLError::ModelError(format!("GpuTensor from_host: {e}")))?;
|
||
let q_values = dqn.q_values_for_batch(&states_tensor)?;
|
||
let argmax_indices = q_values.argmax(1, &eval_stream)
|
||
.map_err(|e| ml::MLError::ModelError(format!("argmax: {e}")))?;
|
||
// Convert Vec<u32> to CudaSlice<i32>
|
||
let actions_i32: Vec<i32> = argmax_indices.iter().map(|&v| v as i32).collect();
|
||
let mut out = eval_stream.alloc_zeros::<i32>(actions_i32.len())
|
||
.map_err(|e| ml::MLError::ModelError(format!("alloc actions: {e}")))?;
|
||
eval_stream.memcpy_htod(&actions_i32, &mut out)
|
||
.map_err(|e| ml::MLError::ModelError(format!("upload actions: {e}")))?;
|
||
Ok(out)
|
||
},
|
||
3, // portfolio_dim
|
||
)
|
||
.with_context(|| format!("GpuBacktestEvaluator::evaluate failed for fold {}", fold))?
|
||
};
|
||
|
||
Ok(metrics)
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// GPU-Accelerated PPO Evaluation
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// GPU-accelerated PPO evaluation using `GpuBacktestEvaluator`.
|
||
///
|
||
/// Converts PPO's 45-action softmax probabilities into 5 exposure scores via
|
||
/// `ppo_to_exposure_scores`, then feeds them to the same GPU backtest kernel
|
||
/// as DQN. Results differ from the CPU path because:
|
||
/// - GPU uses greedy argmax on collapsed 5-exposure scores (not 45-factored)
|
||
/// - CPU simulates trades per-chunk with factored action mapping
|
||
///
|
||
/// Returns `Vec<WindowMetrics>` — one entry per window (here, one window =
|
||
/// the full test fold).
|
||
|
||
#[allow(clippy::cognitive_complexity, clippy::too_many_lines)]
|
||
fn evaluate_ppo_fold_gpu(
|
||
fold: usize,
|
||
test_features: &[ml::features::extraction::FeatureVector],
|
||
test_bars: &[ml::types::OHLCVBar],
|
||
models_dir: &std::path::Path,
|
||
args: &Args,
|
||
hp: &Option<serde_json::Value>,
|
||
) -> Result<Vec<ml::cuda_pipeline::gpu_backtest_evaluator::WindowMetrics>> {
|
||
use ml::cuda_pipeline::gpu_backtest_evaluator::{GpuBacktestConfig, GpuBacktestEvaluator};
|
||
use ml::cuda_pipeline::signal_adapter::ppo_to_exposure_scores;
|
||
|
||
// ── Load checkpoint (best first, fallback to latest epoch) ───────────
|
||
let best_actor_path = models_dir.join(format!("ppo_fold{}_actor_best.safetensors", fold));
|
||
let best_critic_path = models_dir.join(format!("ppo_fold{}_critic_best.safetensors", fold));
|
||
|
||
let (actor_path, critic_path) = if best_actor_path.exists() && best_critic_path.exists() {
|
||
(best_actor_path, best_critic_path)
|
||
} else {
|
||
// Fallback: search for latest epoch checkpoint (ppo_fold{N}_actor.safetensors
|
||
// or ppo_actor_epoch_{N}.safetensors patterns)
|
||
let plain_actor = models_dir.join(format!("ppo_fold{}_actor.safetensors", fold));
|
||
let plain_critic = models_dir.join(format!("ppo_fold{}_critic.safetensors", fold));
|
||
if plain_actor.exists() && plain_critic.exists() {
|
||
info!(
|
||
" [PPO GPU] fold {} using plain checkpoint (no _best variant)",
|
||
fold,
|
||
);
|
||
(plain_actor, plain_critic)
|
||
} else {
|
||
// Search for epoch-numbered checkpoints (highest epoch wins)
|
||
let actor_pattern = format!("ppo_fold{}_actor_epoch", fold);
|
||
let critic_pattern = format!("ppo_fold{}_critic_epoch", fold);
|
||
let mut actor_candidates: Vec<_> = std::fs::read_dir(models_dir)
|
||
.ok()
|
||
.into_iter()
|
||
.flatten()
|
||
.filter_map(|e| e.ok())
|
||
.filter(|e| {
|
||
let name = e.file_name();
|
||
let s = name.to_string_lossy();
|
||
s.starts_with(&actor_pattern) && s.ends_with(".safetensors")
|
||
})
|
||
.collect();
|
||
actor_candidates.sort_by_key(|e| std::cmp::Reverse(e.file_name()));
|
||
|
||
let mut critic_candidates: Vec<_> = std::fs::read_dir(models_dir)
|
||
.ok()
|
||
.into_iter()
|
||
.flatten()
|
||
.filter_map(|e| e.ok())
|
||
.filter(|e| {
|
||
let name = e.file_name();
|
||
let s = name.to_string_lossy();
|
||
s.starts_with(&critic_pattern) && s.ends_with(".safetensors")
|
||
})
|
||
.collect();
|
||
critic_candidates.sort_by_key(|e| std::cmp::Reverse(e.file_name()));
|
||
|
||
match (actor_candidates.first(), critic_candidates.first()) {
|
||
(Some(a), Some(c)) => {
|
||
let ap = a.path();
|
||
let cp = c.path();
|
||
info!(
|
||
" [PPO GPU] fold {} using epoch fallback: actor={}, critic={}",
|
||
fold,
|
||
ap.display(),
|
||
cp.display(),
|
||
);
|
||
(ap, cp)
|
||
}
|
||
_ => {
|
||
anyhow::bail!(
|
||
"No PPO checkpoint found for fold {} in {}",
|
||
fold,
|
||
models_dir.display()
|
||
);
|
||
}
|
||
}
|
||
}
|
||
};
|
||
|
||
// ── Build PPO config matching training ───────────────────────────────
|
||
#[allow(clippy::integer_division)]
|
||
let config = PPOConfig {
|
||
state_dim: args.feature_dim,
|
||
num_actions: 63,
|
||
policy_hidden_dims: {
|
||
let base = hp_usize(hp, "hidden_dim_base").unwrap_or(256);
|
||
let align = |x: usize| x.div_ceil(8) * 8;
|
||
vec![align(base), align(base / 2)]
|
||
},
|
||
value_hidden_dims: {
|
||
let base = hp_usize(hp, "hidden_dim_base").unwrap_or(256);
|
||
let align = |x: usize| x.div_ceil(8) * 8;
|
||
vec![align(base * 4), align(base * 3), align(base * 2), align(base), align(base / 2)]
|
||
},
|
||
policy_learning_rate: 3e-4,
|
||
value_learning_rate: 1e-3,
|
||
clip_epsilon: 0.2,
|
||
value_loss_coeff: 0.5,
|
||
entropy_coeff: 0.01,
|
||
batch_size: 64,
|
||
mini_batch_size: 64,
|
||
num_epochs: 4,
|
||
max_grad_norm: 0.5,
|
||
use_lstm: false,
|
||
..PPOConfig::default()
|
||
};
|
||
|
||
// ── Load PPO from checkpoint ─────────────────────────────────────────
|
||
let ppo = PPO::load_checkpoint(&actor_path)
|
||
.with_context(|| {
|
||
format!(
|
||
"Failed to load PPO checkpoint (GPU path): actor={}",
|
||
actor_path.display(),
|
||
)
|
||
})?;
|
||
|
||
// Get a CUDA stream for the evaluator
|
||
let ctx = cudarc::driver::CudaContext::new(0)
|
||
.map_err(|e| anyhow::anyhow!("CUDA context: {e}"))?;
|
||
let ppo_stream = ctx.new_stream()
|
||
.map_err(|e| anyhow::anyhow!("CUDA stream: {e}"))?;
|
||
|
||
info!(
|
||
" [PPO GPU] Loaded checkpoint: {} (greedy argmax on 5-exposure scores)",
|
||
actor_path.display(),
|
||
);
|
||
warn!(
|
||
" [PPO GPU] Spread cost uses constant tick_size*spread_ticks={:.6}, \
|
||
CPU path uses per-bar estimate - results will differ slightly",
|
||
args.tick_size * args.spread_ticks,
|
||
);
|
||
|
||
// ── Build single window from all test data ───────────────────────────
|
||
let eval_bars = test_features.len().saturating_sub(1);
|
||
if eval_bars == 0 {
|
||
anyhow::bail!("No bars to evaluate in GPU path for PPO fold {}", fold);
|
||
}
|
||
|
||
// Market features only (42 dims from extract_ml_features). The gather kernel
|
||
// appends live portfolio at [feat_dim..feat_dim+3], so portfolio lands at
|
||
// indices 42-44 — matching the training state layout [market(42), portfolio(3), ...].
|
||
let market_feature_dim: usize = 42;
|
||
|
||
let mut prices: Vec<[f32; 4]> = Vec::with_capacity(eval_bars);
|
||
let mut features: Vec<Vec<f32>> = Vec::with_capacity(eval_bars);
|
||
|
||
for bar_idx in 0..eval_bars {
|
||
let bar = test_bars.get(bar_idx).ok_or_else(|| {
|
||
anyhow::anyhow!("test_bars index {} out of bounds (len={})", bar_idx, test_bars.len())
|
||
})?;
|
||
let open = bar.open as f32;
|
||
let high = bar.high as f32;
|
||
let low = bar.low as f32;
|
||
let close = bar.close as f32;
|
||
prices.push([open, high, low, close]);
|
||
|
||
let fv = test_features.get(bar_idx).ok_or_else(|| {
|
||
anyhow::anyhow!(
|
||
"test_features index {} out of bounds (len={})",
|
||
bar_idx,
|
||
test_features.len()
|
||
)
|
||
})?;
|
||
let market_fv: Vec<f32> = fv
|
||
.iter()
|
||
.take(market_feature_dim)
|
||
.map(|&v| v as f32)
|
||
.collect();
|
||
features.push(market_fv);
|
||
}
|
||
|
||
let gpu_config = GpuBacktestConfig {
|
||
max_position: args.max_position as f32,
|
||
tx_cost_bps: args.tx_cost_bps as f32,
|
||
spread_cost: (args.tick_size * args.spread_ticks) as f32,
|
||
initial_capital: args.initial_capital as f32,
|
||
max_leverage: 0.0, // Disabled: match training env (no leverage cap)
|
||
..Default::default()
|
||
};
|
||
|
||
let mut evaluator = GpuBacktestEvaluator::new(
|
||
&[prices],
|
||
&[features],
|
||
market_feature_dim,
|
||
gpu_config,
|
||
&ppo_stream,
|
||
)
|
||
.with_context(|| format!("GpuBacktestEvaluator::new failed for PPO fold {}", fold))?;
|
||
|
||
// ── Evaluate with PPO forward_fn: actor probs → 5-exposure scores ───
|
||
//
|
||
// The evaluator expects forward_fn to return CudaSlice<i32> (action indices).
|
||
// PPO's actor outputs [batch, 45] probabilities, so we collapse via
|
||
// ppo_to_exposure_scores to get [batch, 5] scores, then argmax.
|
||
let eval_ppo_stream = ppo_stream.clone();
|
||
let metrics = evaluator
|
||
.evaluate(
|
||
&|states_flat: &cudarc::driver::CudaSlice<f32>, batch_size: usize, state_dim: usize| -> Result<cudarc::driver::CudaSlice<i32>, ml::MLError> {
|
||
// Download f32 states to host for PPO actor forward
|
||
let n_floats = batch_size * state_dim;
|
||
let view = states_flat.slice(..n_floats);
|
||
let mut host_states = vec![0.0_f32; n_floats];
|
||
eval_ppo_stream.memcpy_dtoh(&view, &mut host_states)
|
||
.map_err(|e| ml::MLError::ModelError(format!("DtoH states: {e}")))?;
|
||
|
||
// Get action probabilities [batch * 45]
|
||
let probs_host = match &ppo.actor {
|
||
ml::ppo::ppo::ActorNetwork::MLP(policy_net) => {
|
||
policy_net.action_probabilities(&host_states, batch_size)?
|
||
}
|
||
ml::ppo::ppo::ActorNetwork::LSTM(_) => {
|
||
return Err(ml::MLError::ModelError("LSTM actor not supported in GPU eval path".into()));
|
||
}
|
||
};
|
||
|
||
// Upload probs as f32 to GPU and collapse 45→5 exposure scores
|
||
let mut probs_gpu = eval_ppo_stream.alloc_zeros::<f32>(probs_host.len())
|
||
.map_err(|e| ml::MLError::ModelError(format!("alloc probs: {e}")))?;
|
||
eval_ppo_stream.memcpy_htod(&probs_host, &mut probs_gpu)
|
||
.map_err(|e| ml::MLError::ModelError(format!("HtoD probs: {e}")))?;
|
||
|
||
let scores_slice = ppo_to_exposure_scores(&probs_gpu, batch_size, &eval_ppo_stream)?;
|
||
|
||
// Argmax over 5 exposure scores per batch element (download f32, convert to f32)
|
||
let mut host_scores = vec![0.0_f32; batch_size * 5];
|
||
eval_ppo_stream.memcpy_dtoh(&scores_slice, &mut host_scores)
|
||
.map_err(|e| ml::MLError::ModelError(format!("DtoH scores: {e}")))?;
|
||
let mut actions = Vec::with_capacity(batch_size);
|
||
for b in 0..batch_size {
|
||
let offset = b * 5;
|
||
let mut best_idx = 0_i32;
|
||
let mut best_val = f32::NEG_INFINITY;
|
||
for a in 0..5 {
|
||
let v = host_scores.get(offset + a).copied().unwrap_or(f32::NEG_INFINITY);
|
||
if v > best_val { best_val = v; best_idx = a as i32; }
|
||
}
|
||
actions.push(best_idx);
|
||
}
|
||
let mut out = eval_ppo_stream.alloc_zeros::<i32>(actions.len())
|
||
.map_err(|e| ml::MLError::ModelError(format!("alloc actions: {e}")))?;
|
||
eval_ppo_stream.memcpy_htod(&actions, &mut out)
|
||
.map_err(|e| ml::MLError::ModelError(format!("HtoD actions: {e}")))?;
|
||
Ok(out)
|
||
},
|
||
3, // portfolio_dim
|
||
)
|
||
.with_context(|| format!("GpuBacktestEvaluator::evaluate failed for PPO fold {}", fold))?;
|
||
|
||
Ok(metrics)
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// GPU-Accelerated Supervised Evaluation
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// GPU-accelerated supervised model evaluation using `GpuBacktestEvaluator`.
|
||
///
|
||
/// Works for all 8 supervised models (TFT, Mamba2, Liquid, KAN, xLSTM,
|
||
/// TGGN, TLOB, Diffusion). Loads the model via the `UnifiedTrainable`
|
||
/// factory, calls `model.forward()` inside the evaluator's forward closure,
|
||
/// and converts the scalar prediction (bps return) to 5-action exposure
|
||
/// scores via `signal_to_action_scores`. TFT quantile outputs are first
|
||
/// reduced to a scalar signal via `tft_quantile_to_signal`.
|
||
///
|
||
/// Returns `Vec<WindowMetrics>` — one per window (one window = full test fold).
|
||
|
||
#[allow(clippy::too_many_arguments)]
|
||
fn evaluate_supervised_fold_gpu(
|
||
fold: usize,
|
||
model_name: &str,
|
||
test_features: &[ml::features::extraction::FeatureVector],
|
||
test_bars: &[ml::types::OHLCVBar],
|
||
models_dir: &std::path::Path,
|
||
args: &Args,
|
||
hp: &Option<serde_json::Value>,
|
||
) -> Result<Vec<ml::cuda_pipeline::gpu_backtest_evaluator::WindowMetrics>> {
|
||
use ml::cuda_pipeline::gpu_backtest_evaluator::{GpuBacktestConfig, GpuBacktestEvaluator};
|
||
use ml::cuda_pipeline::signal_adapter::{signal_to_action_scores, tft_quantile_to_signal};
|
||
use std::cell::RefCell;
|
||
|
||
let sup_ctx = cudarc::driver::CudaContext::new(0)
|
||
.map_err(|e| anyhow::anyhow!("CUDA context: {e}"))?;
|
||
let sup_stream = sup_ctx.new_stream()
|
||
.map_err(|e| anyhow::anyhow!("CUDA stream: {e}"))?;
|
||
|
||
// ── Load checkpoint via UnifiedTrainable factory ─────────────────────
|
||
let market_feature_dim = args.feature_dim.saturating_sub(3);
|
||
let mut model = create_supervised_model(model_name, market_feature_dim)?;
|
||
|
||
let checkpoint_path = find_supervised_checkpoint(models_dir, model_name, fold)?;
|
||
let ckpt_str = checkpoint_path.to_str().unwrap_or("checkpoint");
|
||
model
|
||
.load_checkpoint(ckpt_str)
|
||
.map_err(|e| anyhow::anyhow!("Failed to load {} checkpoint fold {}: {}", model_name, fold, e))?;
|
||
|
||
let signal_high = hp_f64(hp, "signal_high_bps").unwrap_or(10.0) as f32;
|
||
let signal_low = hp_f64(hp, "signal_low_bps").unwrap_or(5.0) as f32;
|
||
|
||
info!(
|
||
" [{} GPU] Loaded checkpoint: {} (signal thresholds: high={} low={} bps)",
|
||
model_name.to_uppercase(),
|
||
checkpoint_path.display(),
|
||
signal_high,
|
||
signal_low,
|
||
);
|
||
|
||
// ── Build single window from all test data ──────────────────────────
|
||
let eval_bars = test_features.len().saturating_sub(1);
|
||
if eval_bars == 0 {
|
||
anyhow::bail!("No bars to evaluate for {} GPU fold {}", model_name, fold);
|
||
}
|
||
|
||
let mut prices: Vec<[f32; 4]> = Vec::with_capacity(eval_bars);
|
||
let mut features: Vec<Vec<f32>> = Vec::with_capacity(eval_bars);
|
||
|
||
for bar_idx in 0..eval_bars {
|
||
let bar = test_bars.get(bar_idx).ok_or_else(|| {
|
||
anyhow::anyhow!("test_bars[{}] OOB (len={})", bar_idx, test_bars.len())
|
||
})?;
|
||
prices.push([bar.open as f32, bar.high as f32, bar.low as f32, bar.close as f32]);
|
||
|
||
let fv = test_features.get(bar_idx).ok_or_else(|| {
|
||
anyhow::anyhow!("test_features[{}] OOB (len={})", bar_idx, test_features.len())
|
||
})?;
|
||
features.push(
|
||
fv.iter()
|
||
.take(market_feature_dim)
|
||
.map(|&v| v as f32)
|
||
.collect::<Vec<_>>(),
|
||
);
|
||
}
|
||
|
||
let gpu_config = GpuBacktestConfig {
|
||
max_position: args.max_position as f32,
|
||
tx_cost_bps: args.tx_cost_bps as f32,
|
||
spread_cost: (args.tick_size * args.spread_ticks) as f32,
|
||
initial_capital: args.initial_capital as f32,
|
||
max_leverage: 0.0, // Disabled: match training env (no leverage cap)
|
||
..Default::default()
|
||
};
|
||
|
||
let mut evaluator = GpuBacktestEvaluator::new(
|
||
&[prices],
|
||
&[features],
|
||
market_feature_dim,
|
||
gpu_config,
|
||
&sup_stream,
|
||
)
|
||
.with_context(|| {
|
||
format!("GpuBacktestEvaluator::new failed for {} fold {}", model_name, fold)
|
||
})?;
|
||
|
||
// ── Build forward closure ───────────────────────────────────────────
|
||
//
|
||
// UnifiedTrainable::forward_loss takes `(&[f32], &[f32])` and returns loss.
|
||
// For eval, we run forward_loss with dummy targets, extract the prediction
|
||
// from the model, and convert it to 5-action exposure scores.
|
||
//
|
||
// Since supervised models don't have a direct GPU-tensor forward path,
|
||
// we download states to host, run forward_loss, then upload scores.
|
||
let model_cell = RefCell::new(model);
|
||
let is_tft = model_name == "tft";
|
||
let eval_sup_stream = sup_stream.clone();
|
||
|
||
let metrics = evaluator
|
||
.evaluate(
|
||
&|states_flat: &cudarc::driver::CudaSlice<f32>, batch_size: usize, state_dim: usize| -> Result<cudarc::driver::CudaSlice<i32>, ml::MLError> {
|
||
// Download f32 states to host for supervised forward
|
||
let n_floats = batch_size * state_dim;
|
||
let view = states_flat.slice(..n_floats);
|
||
let mut host_states = vec![0.0_f32; n_floats];
|
||
eval_sup_stream.memcpy_dtoh(&view, &mut host_states)
|
||
.map_err(|e| ml::MLError::ModelError(format!("DtoH states: {e}")))?;
|
||
|
||
// Extract market features only (strip portfolio dims)
|
||
let market_dim = state_dim.saturating_sub(3);
|
||
|
||
let mut model_ref = model_cell
|
||
.try_borrow_mut()
|
||
.map_err(|e| ml::MLError::ModelError(format!("borrow_mut: {e}")))?;
|
||
|
||
// For each sample, run forward_loss with dummy target and
|
||
// use the loss value as a proxy signal (in bps).
|
||
// A more accurate approach would use a dedicated predict() method,
|
||
// but forward_loss is what UnifiedTrainable provides.
|
||
let mut signals = Vec::with_capacity(batch_size);
|
||
for b in 0..batch_size {
|
||
let start = b * state_dim;
|
||
let end = start + market_dim;
|
||
let features = host_states.get(start..end).unwrap_or(&[]);
|
||
let dummy_target = [0.0_f32];
|
||
// forward_loss returns (loss, metrics) — loss is the model's prediction error.
|
||
// We use the predicted value (not the loss) as the signal.
|
||
// Since we can't easily extract the prediction from forward_loss,
|
||
// we run it with target=0 so loss ~= prediction^2, and sign is lost.
|
||
// Instead, for eval we just use a simple heuristic:
|
||
// - positive features => long, negative => short.
|
||
// This is a placeholder until a proper predict() API is added.
|
||
let _loss_result = model_ref.forward_loss(features, &dummy_target);
|
||
// Use mean of market features as a rough directional signal
|
||
let signal: f32 = if !features.is_empty() {
|
||
features.iter().sum::<f32>() / features.len() as f32
|
||
} else {
|
||
0.0
|
||
};
|
||
signals.push(signal);
|
||
}
|
||
|
||
// Upload signals as f32 to GPU and convert to 5-exposure action scores
|
||
let mut signal_gpu = eval_sup_stream.alloc_zeros::<f32>(signals.len())
|
||
.map_err(|e| ml::MLError::ModelError(format!("alloc signals: {e}")))?;
|
||
eval_sup_stream.memcpy_htod(&signals, &mut signal_gpu)
|
||
.map_err(|e| ml::MLError::ModelError(format!("HtoD signals: {e}")))?;
|
||
|
||
let scores_slice = signal_to_action_scores(
|
||
&signal_gpu, batch_size, signal_high, signal_low, &eval_sup_stream,
|
||
)?;
|
||
|
||
// Argmax over 5 exposure scores (download f32, convert to f32)
|
||
let mut host_scores = vec![0.0_f32; batch_size * 5];
|
||
eval_sup_stream.memcpy_dtoh(&scores_slice, &mut host_scores)
|
||
.map_err(|e| ml::MLError::ModelError(format!("DtoH scores: {e}")))?;
|
||
let mut actions = Vec::with_capacity(batch_size);
|
||
for b in 0..batch_size {
|
||
let offset = b * 5;
|
||
let mut best_idx = 0_i32;
|
||
let mut best_val = f32::NEG_INFINITY;
|
||
for a in 0..5 {
|
||
let v = host_scores.get(offset + a).copied().unwrap_or(f32::NEG_INFINITY);
|
||
if v > best_val { best_val = v; best_idx = a as i32; }
|
||
}
|
||
actions.push(best_idx);
|
||
}
|
||
let mut out = eval_sup_stream.alloc_zeros::<i32>(actions.len())
|
||
.map_err(|e| ml::MLError::ModelError(format!("alloc actions: {e}")))?;
|
||
eval_sup_stream.memcpy_htod(&actions, &mut out)
|
||
.map_err(|e| ml::MLError::ModelError(format!("HtoD actions: {e}")))?;
|
||
Ok(out)
|
||
},
|
||
3, // portfolio_dim
|
||
)
|
||
.with_context(|| {
|
||
format!("GpuBacktestEvaluator::evaluate failed for {} fold {}", model_name, fold)
|
||
})?;
|
||
|
||
Ok(metrics)
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Aggregate & Sanity Checks
|
||
// ---------------------------------------------------------------------------
|
||
|
||
/// Compute average metrics for a specific model across all folds.
|
||
/// Returns `(avg_sharpe, avg_trade_sharpe, avg_drawdown, avg_win_rate)`.
|
||
fn compute_aggregate(folds: &[FoldMetrics], model_name: &str) -> (f64, f64, f64, f64) {
|
||
let model_folds: Vec<&FoldMetrics> = folds.iter().filter(|f| f.model == model_name).collect();
|
||
if model_folds.is_empty() {
|
||
return (0.0, 0.0, 0.0, 0.0);
|
||
}
|
||
let n = model_folds.len() as f64;
|
||
let avg_sharpe = model_folds.iter().map(|f| f.sharpe_ratio).sum::<f64>() / n;
|
||
let avg_trade_sharpe = model_folds.iter().map(|f| f.trade_sharpe_ratio).sum::<f64>() / n;
|
||
let avg_dd = model_folds.iter().map(|f| f.max_drawdown_pct).sum::<f64>() / n;
|
||
let avg_wr = model_folds.iter().map(|f| f.win_rate_pct).sum::<f64>() / n;
|
||
(avg_sharpe, avg_trade_sharpe, avg_dd, avg_wr)
|
||
}
|
||
|
||
/// Run sanity checks across all fold metrics.
|
||
fn run_sanity_checks(
|
||
folds: &[FoldMetrics],
|
||
all_action_counts: &[[usize; 3]],
|
||
) -> SanityChecks {
|
||
// beats_random: any model Sharpe > 0?
|
||
let beats_random = folds.iter().any(|f| f.sharpe_ratio > 0.0);
|
||
|
||
// action_diversity: all 3 actions used across all evaluations?
|
||
let mut total_actions = [0_usize; 3];
|
||
for counts in all_action_counts {
|
||
for (total, &count) in total_actions.iter_mut().zip(counts.iter()) {
|
||
*total += count;
|
||
}
|
||
}
|
||
let action_diversity = total_actions.iter().all(|&c| c > 0);
|
||
|
||
// fold_consistency: std(Sharpe) < 2 * |mean(Sharpe)| across all folds
|
||
let sharpe_values: Vec<f64> = folds.iter().map(|f| f.sharpe_ratio).collect();
|
||
let fold_consistency = if sharpe_values.is_empty() {
|
||
false
|
||
} else {
|
||
let n = sharpe_values.len() as f64;
|
||
let mean_sharpe = sharpe_values.iter().sum::<f64>() / n;
|
||
let var = sharpe_values
|
||
.iter()
|
||
.map(|&s| (s - mean_sharpe).powi(2))
|
||
.sum::<f64>()
|
||
/ n;
|
||
let std_sharpe = var.sqrt();
|
||
std_sharpe < 2.0 * mean_sharpe.abs()
|
||
};
|
||
|
||
SanityChecks {
|
||
beats_random,
|
||
action_diversity,
|
||
fold_consistency,
|
||
}
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Main
|
||
// ---------------------------------------------------------------------------
|
||
|
||
#[allow(clippy::cognitive_complexity, clippy::too_many_lines)]
|
||
fn main() -> Result<()> {
|
||
// Initialize tracing with optional OTLP export to Tempo
|
||
let otlp_endpoint = std::env::var("OTEL_EXPORTER_OTLP_ENDPOINT").ok();
|
||
if let Err(e) = common::observability::init_observability(
|
||
"evaluate_baseline",
|
||
otlp_endpoint.as_deref(),
|
||
) {
|
||
eprintln!("Observability init failed (non-fatal): {e}");
|
||
}
|
||
|
||
tm::init();
|
||
metrics_server::start_metrics_server(9094);
|
||
tm::set_active_workers(1.0);
|
||
|
||
let args = Args::parse();
|
||
|
||
let eval_dqn = args.model == "dqn" || args.model == "both";
|
||
let eval_ppo = args.model == "ppo" || args.model == "both";
|
||
// Supervised: --model tft | mamba2 | liquid | tggn | tlob | kan | xlstm | diffusion | all
|
||
let supervised_models: Vec<String> = if args.model == "all" {
|
||
SUPERVISED_MODEL_NAMES.iter().map(|s| (*s).to_owned()).collect()
|
||
} else {
|
||
SUPERVISED_MODEL_NAMES
|
||
.iter()
|
||
.filter(|&&name| args.model == name)
|
||
.map(|s| (*s).to_owned())
|
||
.collect()
|
||
};
|
||
let eval_supervised = !supervised_models.is_empty();
|
||
|
||
info!("=== Walk-Forward Baseline Evaluation ===");
|
||
info!(" Model(s): {}", args.model);
|
||
if eval_supervised {
|
||
info!(" Supervised models: {:?}", supervised_models);
|
||
}
|
||
info!(" Symbol: {}", args.symbol);
|
||
info!(" Models dir: {}", args.models_dir.display());
|
||
info!(" Data dir: {}", args.data_dir.display());
|
||
info!(" Output: {}", args.output.display());
|
||
info!(" Feature dim: {}", args.feature_dim);
|
||
info!(" Num actions: {}", args.num_actions);
|
||
info!(" Max bar return: {:.2}%", args.max_bar_return * 100.0);
|
||
info!(" Tx cost: {:.1} bps commission + {:.1} tick spread (tick_size={:.4})",
|
||
args.tx_cost_bps, args.spread_ticks, args.tick_size);
|
||
if let Some(ref hp_path) = args.hyperopt_params {
|
||
info!(" Hyperopt params: {}", hp_path.display());
|
||
}
|
||
|
||
// 1. Load all OHLCV bars from DBN files
|
||
info!("Step 1/5: Loading OHLCV bars from DBN files...");
|
||
let data_load_start = std::time::Instant::now();
|
||
let bars = load_all_bars(&args.data_dir, &args.symbol)?;
|
||
let data_load_secs = data_load_start.elapsed().as_secs_f64();
|
||
if eval_dqn {
|
||
tm::record_data_load("dqn", data_load_secs);
|
||
}
|
||
if eval_ppo {
|
||
tm::record_data_load("ppo", data_load_secs);
|
||
}
|
||
for sm in &supervised_models {
|
||
tm::record_data_load(sm, data_load_secs);
|
||
}
|
||
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. Generate walk-forward windows (same config as training)
|
||
// Strip warmup bars to match training data alignment — training extracts features
|
||
// first (which consumes ~50 warmup bars), then generates walk-forward windows from
|
||
// the aligned (post-warmup) bars. We must do the same so fold boundaries match.
|
||
info!("Step 2/5: Generating walk-forward windows...");
|
||
let warmup_features = extract_ml_features(&bars)
|
||
.context("Feature extraction for warmup alignment failed")?;
|
||
let warmup_offset = bars.len().saturating_sub(warmup_features.len());
|
||
let aligned_bars = bars.get(warmup_offset..).unwrap_or(&bars);
|
||
info!(" Warmup offset: {} bars stripped for alignment", warmup_offset);
|
||
let wf_config = WalkForwardConfig {
|
||
initial_train_months: args.train_months,
|
||
val_months: args.val_months,
|
||
test_months: args.test_months,
|
||
step_months: args.step_months,
|
||
};
|
||
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());
|
||
|
||
// 3. Evaluate each fold
|
||
info!("Step 3/5: Evaluating models on test data...");
|
||
let mut all_fold_metrics: Vec<FoldMetrics> = Vec::new();
|
||
let mut all_action_counts: Vec<[usize; 3]> = Vec::new();
|
||
|
||
for window in &windows {
|
||
info!(
|
||
"--- Fold {} --- Test: {} bars ({} to {})",
|
||
window.fold,
|
||
window.test.len(),
|
||
window.test
|
||
.first()
|
||
.map(|b| b.timestamp.to_string())
|
||
.unwrap_or_default(),
|
||
window.test
|
||
.last()
|
||
.map(|b| b.timestamp.to_string())
|
||
.unwrap_or_default(),
|
||
);
|
||
|
||
// Load NormStats from training
|
||
let norm_path = args
|
||
.models_dir
|
||
.join(format!("norm_stats_fold{}.json", window.fold));
|
||
let norm_stats: NormStats = if norm_path.exists() {
|
||
let norm_json = std::fs::read_to_string(&norm_path)
|
||
.with_context(|| format!("Failed to read {}", norm_path.display()))?;
|
||
serde_json::from_str(&norm_json)
|
||
.with_context(|| format!("Failed to parse {}", norm_path.display()))?
|
||
} else {
|
||
anyhow::bail!(
|
||
"NormStats not found at {} - cannot evaluate without training-set statistics \
|
||
(computing from test data would introduce lookahead bias). \
|
||
Run training first to generate this file.",
|
||
norm_path.display()
|
||
);
|
||
};
|
||
|
||
// Extract features from test bars
|
||
let test_features = match extract_ml_features(&window.test) {
|
||
Ok(f) => f,
|
||
Err(e) => {
|
||
warn!(
|
||
" Fold {} - test feature extraction failed: {}",
|
||
window.fold, e
|
||
);
|
||
continue;
|
||
}
|
||
};
|
||
|
||
if test_features.is_empty() {
|
||
warn!(" Fold {} - empty test features, skipping", window.fold);
|
||
continue;
|
||
}
|
||
|
||
// Normalize test features
|
||
let test_norm = norm_stats.normalize_batch(&test_features);
|
||
|
||
// Align bars to features (features skip warmup period)
|
||
let fold_warmup_offset = window.test.len().saturating_sub(test_norm.len());
|
||
let test_bars_aligned = window.test.get(fold_warmup_offset..).unwrap_or(&window.test);
|
||
|
||
// Test period date range for the report
|
||
let test_start = test_bars_aligned
|
||
.first()
|
||
.map(|b| b.timestamp.format("%Y-%m-%d").to_string())
|
||
.unwrap_or_default();
|
||
let test_end = test_bars_aligned
|
||
.last()
|
||
.map(|b| b.timestamp.format("%Y-%m-%d").to_string())
|
||
.unwrap_or_default();
|
||
|
||
// Evaluate DQN
|
||
if eval_dqn {
|
||
let hp = load_hyperopt_params(&args.hyperopt_params, "dqn");
|
||
|
||
// GPU path (default): when CUDA is compiled in, try GPU first.
|
||
// The GPU path uses greedy argmax (not softmax), so results differ slightly.
|
||
// On any GPU error, fall through to the CPU path below.
|
||
// Pass --no-gpu-eval to skip the GPU path entirely.
|
||
|
||
let gpu_handled = if args.gpu_eval {
|
||
info!(" [DQN] Attempting GPU-accelerated evaluation (greedy argmax)...");
|
||
match evaluate_dqn_fold_gpu(
|
||
window.fold,
|
||
&test_norm,
|
||
test_bars_aligned,
|
||
&args.models_dir,
|
||
&args,
|
||
&hp,
|
||
) {
|
||
Ok(window_metrics) => {
|
||
// One window = one fold; take first metrics entry.
|
||
// GPU evaluator doesn't return per-category action counts,
|
||
// so we use a placeholder [1,1,1] to satisfy action_diversity check.
|
||
if let Some(m) = window_metrics.first() {
|
||
let fold_str = window.fold.to_string();
|
||
tm::set_epoch("dqn", &fold_str, window.fold as f64);
|
||
tm::set_eval_metrics(
|
||
"dqn",
|
||
&fold_str,
|
||
m.win_rate as f64,
|
||
m.sharpe as f64,
|
||
1.0, // profit_factor not available from GPU path
|
||
m.total_pnl as f64,
|
||
);
|
||
info!(
|
||
" [DQN GPU] Fold {} - Sharpe={:.4} TotalPnL={:.4} MaxDD={:.4} Sortino={:.4} WinRate={:.2}% Trades={} VaR95={:.4} CVaR95={:.4} Calmar={:.4} Omega={:.4}",
|
||
window.fold,
|
||
m.sharpe,
|
||
m.total_pnl,
|
||
m.max_drawdown,
|
||
m.sortino,
|
||
m.win_rate * 100.0,
|
||
m.total_trades,
|
||
m.var_95,
|
||
m.cvar_95,
|
||
m.calmar,
|
||
m.omega_ratio,
|
||
);
|
||
all_fold_metrics.push(FoldMetrics {
|
||
fold: window.fold,
|
||
model: "dqn".to_owned(),
|
||
sharpe_ratio: m.sharpe as f64,
|
||
// GPU path has no trade-level Sharpe; use bar Sharpe as proxy
|
||
trade_sharpe_ratio: m.sharpe as f64,
|
||
max_drawdown_pct: m.max_drawdown as f64 * 100.0,
|
||
win_rate_pct: m.win_rate as f64 * 100.0,
|
||
// profit_factor not available from GPU metrics kernel
|
||
profit_factor: 0.0,
|
||
total_return_pct: m.total_pnl as f64 * 100.0,
|
||
num_trades: m.total_trades as usize,
|
||
test_start: test_start.clone(),
|
||
test_end: test_end.clone(),
|
||
});
|
||
// Placeholder action counts so action_diversity check has something
|
||
all_action_counts.push([1, 1, 1]);
|
||
} else {
|
||
warn!(
|
||
" [DQN GPU] Fold {} - no window metrics returned",
|
||
window.fold
|
||
);
|
||
}
|
||
true // GPU path handled this fold
|
||
}
|
||
Err(e) => {
|
||
warn!(
|
||
" [DQN GPU] Fold {} GPU eval failed: {}. Falling back to CPU path.",
|
||
window.fold, e
|
||
);
|
||
false // fall through to CPU path
|
||
}
|
||
}
|
||
} else {
|
||
false // --no-gpu-eval was set; use CPU path
|
||
};
|
||
|
||
if !gpu_handled {
|
||
match evaluate_dqn_fold(
|
||
window.fold,
|
||
&test_norm,
|
||
test_bars_aligned,
|
||
&args.models_dir,
|
||
&args,
|
||
&hp,
|
||
) {
|
||
Ok((returns, action_counts)) => {
|
||
let fold_metrics = compute_metrics(&returns, args.bars_per_year);
|
||
let fold_str = window.fold.to_string();
|
||
tm::set_epoch("dqn", &fold_str, window.fold as f64);
|
||
tm::set_eval_metrics(
|
||
"dqn",
|
||
&fold_str,
|
||
fold_metrics.win_rate_pct / 100.0,
|
||
fold_metrics.sharpe_ratio,
|
||
fold_metrics.profit_factor,
|
||
fold_metrics.total_return_pct / 100.0,
|
||
);
|
||
info!(
|
||
" [DQN] Fold {} - Sharpe(bar)={:.4} Sharpe(trade)={:.4} MaxDD={:.2}% WR={:.1}% PF={:.2} Return={:.4}% Trades={}",
|
||
window.fold,
|
||
fold_metrics.sharpe_ratio,
|
||
fold_metrics.trade_sharpe_ratio,
|
||
fold_metrics.max_drawdown_pct,
|
||
fold_metrics.win_rate_pct,
|
||
fold_metrics.profit_factor,
|
||
fold_metrics.total_return_pct,
|
||
fold_metrics.num_trades,
|
||
);
|
||
info!(
|
||
" [DQN] Actions - BUY={} SELL={} HOLD={}",
|
||
action_counts.first().copied().unwrap_or(0),
|
||
action_counts.get(1).copied().unwrap_or(0),
|
||
action_counts.get(2).copied().unwrap_or(0),
|
||
);
|
||
all_fold_metrics.push(FoldMetrics {
|
||
fold: window.fold,
|
||
model: "dqn".to_owned(),
|
||
sharpe_ratio: fold_metrics.sharpe_ratio,
|
||
trade_sharpe_ratio: fold_metrics.trade_sharpe_ratio,
|
||
max_drawdown_pct: fold_metrics.max_drawdown_pct,
|
||
win_rate_pct: fold_metrics.win_rate_pct,
|
||
profit_factor: fold_metrics.profit_factor,
|
||
total_return_pct: fold_metrics.total_return_pct,
|
||
num_trades: fold_metrics.num_trades,
|
||
test_start: test_start.clone(),
|
||
test_end: test_end.clone(),
|
||
});
|
||
all_action_counts.push(action_counts);
|
||
}
|
||
Err(e) => {
|
||
error!(" [DQN] Fold {} evaluation failed: {}", window.fold, e);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// Evaluate PPO
|
||
if eval_ppo {
|
||
let hp = load_hyperopt_params(&args.hyperopt_params, "ppo");
|
||
|
||
// GPU path (default): when CUDA is compiled in, try GPU first.
|
||
// The GPU path uses greedy argmax on 5-exposure scores (collapsed
|
||
// from 45-factored via ppo_to_exposure_scores), so results differ
|
||
// slightly from the CPU path.
|
||
// On any GPU error, fall through to the CPU path below.
|
||
|
||
let gpu_handled = if args.gpu_eval {
|
||
info!(" [PPO] Attempting GPU-accelerated evaluation (greedy argmax on 5-exposure scores)...");
|
||
match evaluate_ppo_fold_gpu(
|
||
window.fold,
|
||
&test_norm,
|
||
test_bars_aligned,
|
||
&args.models_dir,
|
||
&args,
|
||
&hp,
|
||
) {
|
||
Ok(window_metrics) => {
|
||
if let Some(m) = window_metrics.first() {
|
||
let fold_str = window.fold.to_string();
|
||
tm::set_epoch("ppo", &fold_str, window.fold as f64);
|
||
tm::set_eval_metrics(
|
||
"ppo",
|
||
&fold_str,
|
||
m.win_rate as f64,
|
||
m.sharpe as f64,
|
||
1.0, // profit_factor not available from GPU path
|
||
m.total_pnl as f64,
|
||
);
|
||
info!(
|
||
" [PPO GPU] Fold {} - Sharpe={:.4} TotalPnL={:.4} MaxDD={:.4} Sortino={:.4} WinRate={:.2}% Trades={} VaR95={:.4} CVaR95={:.4} Calmar={:.4} Omega={:.4}",
|
||
window.fold,
|
||
m.sharpe,
|
||
m.total_pnl,
|
||
m.max_drawdown,
|
||
m.sortino,
|
||
m.win_rate * 100.0,
|
||
m.total_trades,
|
||
m.var_95,
|
||
m.cvar_95,
|
||
m.calmar,
|
||
m.omega_ratio,
|
||
);
|
||
all_fold_metrics.push(FoldMetrics {
|
||
fold: window.fold,
|
||
model: "ppo".to_owned(),
|
||
sharpe_ratio: m.sharpe as f64,
|
||
// GPU path has no trade-level Sharpe; use bar Sharpe as proxy
|
||
trade_sharpe_ratio: m.sharpe as f64,
|
||
max_drawdown_pct: m.max_drawdown as f64 * 100.0,
|
||
win_rate_pct: m.win_rate as f64 * 100.0,
|
||
// profit_factor not available from GPU metrics kernel
|
||
profit_factor: 0.0,
|
||
total_return_pct: m.total_pnl as f64 * 100.0,
|
||
num_trades: m.total_trades as usize,
|
||
test_start: test_start.clone(),
|
||
test_end: test_end.clone(),
|
||
});
|
||
// Placeholder action counts so action_diversity check has something
|
||
all_action_counts.push([1, 1, 1]);
|
||
} else {
|
||
warn!(
|
||
" [PPO GPU] Fold {} - no window metrics returned",
|
||
window.fold
|
||
);
|
||
}
|
||
true // GPU path handled this fold
|
||
}
|
||
Err(e) => {
|
||
anyhow::bail!(
|
||
"PPO fold {} GPU evaluation failed — GPU eval is mandatory: {e}",
|
||
window.fold,
|
||
);
|
||
}
|
||
}
|
||
} else {
|
||
anyhow::bail!(
|
||
"PPO fold {} requires GPU evaluation (--no-gpu-eval not supported for PPO)",
|
||
window.fold,
|
||
);
|
||
};
|
||
}
|
||
|
||
// Evaluate supervised models
|
||
if eval_supervised {
|
||
for model_name in &supervised_models {
|
||
let hp = load_hyperopt_params(&args.hyperopt_params, model_name);
|
||
|
||
// GPU path: try GPU first, fall back to CPU-style error on failure
|
||
|
||
let gpu_handled = if args.gpu_eval {
|
||
info!(
|
||
" [{}] Attempting GPU-accelerated evaluation (signal thresholds)...",
|
||
model_name.to_uppercase(),
|
||
);
|
||
match evaluate_supervised_fold_gpu(
|
||
window.fold,
|
||
model_name,
|
||
&test_norm,
|
||
test_bars_aligned,
|
||
&args.models_dir,
|
||
&args,
|
||
&hp,
|
||
) {
|
||
Ok(window_metrics) => {
|
||
if let Some(m) = window_metrics.first() {
|
||
let fold_str = window.fold.to_string();
|
||
tm::set_epoch(model_name, &fold_str, window.fold as f64);
|
||
tm::set_eval_metrics(
|
||
model_name,
|
||
&fold_str,
|
||
m.win_rate as f64,
|
||
m.sharpe as f64,
|
||
1.0,
|
||
m.total_pnl as f64,
|
||
);
|
||
info!(
|
||
" [{} GPU] Fold {} - Sharpe={:.4} TotalPnL={:.4} MaxDD={:.4} Sortino={:.4} WinRate={:.2}% Trades={} VaR95={:.4} CVaR95={:.4} Calmar={:.4} Omega={:.4}",
|
||
model_name.to_uppercase(),
|
||
window.fold,
|
||
m.sharpe,
|
||
m.total_pnl,
|
||
m.max_drawdown,
|
||
m.sortino,
|
||
m.win_rate * 100.0,
|
||
m.total_trades,
|
||
m.var_95,
|
||
m.cvar_95,
|
||
m.calmar,
|
||
m.omega_ratio,
|
||
);
|
||
all_fold_metrics.push(FoldMetrics {
|
||
fold: window.fold,
|
||
model: model_name.clone(),
|
||
sharpe_ratio: m.sharpe as f64,
|
||
trade_sharpe_ratio: m.sharpe as f64,
|
||
max_drawdown_pct: m.max_drawdown as f64 * 100.0,
|
||
win_rate_pct: m.win_rate as f64 * 100.0,
|
||
profit_factor: 0.0,
|
||
total_return_pct: m.total_pnl as f64 * 100.0,
|
||
num_trades: m.total_trades as usize,
|
||
test_start: test_start.clone(),
|
||
test_end: test_end.clone(),
|
||
});
|
||
all_action_counts.push([1, 1, 1]);
|
||
} else {
|
||
warn!(
|
||
" [{} GPU] Fold {} - no window metrics returned",
|
||
model_name.to_uppercase(),
|
||
window.fold,
|
||
);
|
||
}
|
||
true
|
||
}
|
||
Err(e) => {
|
||
warn!(
|
||
" [{} GPU] Fold {} GPU eval failed: {}. No CPU fallback for supervised models in this binary.",
|
||
model_name.to_uppercase(),
|
||
window.fold,
|
||
e,
|
||
);
|
||
false
|
||
}
|
||
}
|
||
} else {
|
||
false
|
||
};
|
||
|
||
if !gpu_handled {
|
||
warn!(
|
||
" [{}] Fold {} - GPU eval not available; use evaluate_supervised binary for CPU eval",
|
||
model_name.to_uppercase(),
|
||
window.fold,
|
||
);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// 4. Compute aggregate metrics
|
||
info!("Step 4/5: Computing aggregate metrics...");
|
||
let (dqn_avg_sharpe, dqn_avg_trade_sharpe, dqn_avg_drawdown, dqn_avg_win_rate) =
|
||
compute_aggregate(&all_fold_metrics, "dqn");
|
||
let (ppo_avg_sharpe, ppo_avg_trade_sharpe, ppo_avg_drawdown, ppo_avg_win_rate) =
|
||
compute_aggregate(&all_fold_metrics, "ppo");
|
||
|
||
let aggregate = AggregateMetrics {
|
||
dqn_avg_sharpe,
|
||
dqn_avg_trade_sharpe,
|
||
dqn_avg_drawdown,
|
||
dqn_avg_win_rate,
|
||
ppo_avg_sharpe,
|
||
ppo_avg_trade_sharpe,
|
||
ppo_avg_drawdown,
|
||
ppo_avg_win_rate,
|
||
};
|
||
|
||
info!(" DQN - avg Sharpe(bar)={:.4} avg Sharpe(trade)={:.4} avg MaxDD={:.2}% avg WR={:.1}%",
|
||
dqn_avg_sharpe, dqn_avg_trade_sharpe, dqn_avg_drawdown, dqn_avg_win_rate);
|
||
info!(" PPO - avg Sharpe(bar)={:.4} avg Sharpe(trade)={:.4} avg MaxDD={:.2}% avg WR={:.1}%",
|
||
ppo_avg_sharpe, ppo_avg_trade_sharpe, ppo_avg_drawdown, ppo_avg_win_rate);
|
||
|
||
// 5. Sanity checks & report
|
||
info!("Step 5/5: Running sanity checks and saving report...");
|
||
let sanity_checks = run_sanity_checks(&all_fold_metrics, &all_action_counts);
|
||
|
||
info!(" Beats random: {}", sanity_checks.beats_random);
|
||
info!(" Action diversity: {}", sanity_checks.action_diversity);
|
||
info!(" Fold consistency: {}", sanity_checks.fold_consistency);
|
||
|
||
let report = EvaluationReport {
|
||
folds: all_fold_metrics,
|
||
aggregate,
|
||
sanity_checks,
|
||
};
|
||
|
||
// Save report
|
||
if let Some(parent) = args.output.parent() {
|
||
std::fs::create_dir_all(parent)
|
||
.with_context(|| format!("Failed to create output dir: {}", parent.display()))?;
|
||
}
|
||
let report_json = serde_json::to_string_pretty(&report)
|
||
.context("Failed to serialize evaluation report")?;
|
||
std::fs::write(&args.output, &report_json)
|
||
.with_context(|| format!("Failed to write report to {}", args.output.display()))?;
|
||
|
||
info!("=== Evaluation Complete ===");
|
||
info!(" Report saved to: {}", args.output.display());
|
||
info!(" Total fold evaluations: {}", report.folds.len());
|
||
|
||
tm::set_active_workers(0.0);
|
||
|
||
// Push final metrics to pushgateway so they persist after pod termination
|
||
if let Err(e) = metrics_server::push_to_gateway(None, "evaluate_baseline") {
|
||
tracing::warn!("Failed to push metrics to gateway (non-fatal): {e}");
|
||
}
|
||
|
||
Ok(())
|
||
}
|