d78fe9d4a8
precompute_features prints Format: f32 (v2), OFI_DIM=20 (was hardcoded v1). FXCACHE_VERSION exported as pub const for external use. graph_adam_m/v/step removed (dead buffers). q_mean_ema_ptr removed from c51_grad_kernel (ISV replaced Q-drift penalty). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
542 lines
23 KiB
Rust
542 lines
23 KiB
Rust
#![allow(
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clippy::assertions_on_constants,
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clippy::assertions_on_result_states,
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clippy::clone_on_copy,
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clippy::decimal_literal_representation,
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clippy::doc_markdown,
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clippy::empty_line_after_doc_comments,
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clippy::field_reassign_with_default,
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clippy::get_unwrap,
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clippy::identity_op,
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clippy::inconsistent_digit_grouping,
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clippy::indexing_slicing,
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clippy::integer_division,
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clippy::len_zero,
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clippy::let_underscore_must_use,
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clippy::manual_div_ceil,
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clippy::manual_let_else,
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clippy::manual_range_contains,
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clippy::modulo_arithmetic,
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clippy::needless_range_loop,
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clippy::non_ascii_literal,
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clippy::redundant_clone,
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clippy::shadow_reuse,
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clippy::shadow_same,
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clippy::shadow_unrelated,
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clippy::single_match_else,
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clippy::str_to_string,
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clippy::string_slice,
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clippy::tests_outside_test_module,
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clippy::too_many_lines,
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clippy::unnecessary_wraps,
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clippy::unseparated_literal_suffix,
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clippy::use_debug,
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clippy::useless_vec,
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clippy::wildcard_enum_match_arm,
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clippy::else_if_without_else,
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clippy::expect_used,
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clippy::missing_const_for_fn,
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clippy::similar_names,
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clippy::type_complexity,
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clippy::collapsible_else_if,
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clippy::doc_lazy_continuation,
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clippy::items_after_test_module,
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clippy::map_clone,
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clippy::multiple_unsafe_ops_per_block,
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clippy::unwrap_or_default,
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clippy::assign_op_pattern,
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clippy::needless_borrow,
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clippy::println_empty_string,
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clippy::unnecessary_cast,
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clippy::used_underscore_binding,
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clippy::create_dir,
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clippy::implicit_saturating_sub,
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clippy::exit,
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clippy::expect_fun_call,
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clippy::too_many_arguments,
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clippy::unnecessary_map_or,
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clippy::unwrap_used,
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dead_code,
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unused_imports,
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unused_variables,
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clippy::cloned_ref_to_slice_refs,
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clippy::neg_multiply,
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clippy::while_let_loop,
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clippy::bool_assert_comparison,
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clippy::excessive_precision,
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clippy::trivially_copy_pass_by_ref,
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clippy::op_ref,
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clippy::redundant_closure,
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clippy::unnecessary_lazy_evaluations,
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clippy::if_then_some_else_none,
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clippy::unnecessary_to_owned,
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clippy::single_component_path_imports,
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)]
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//! Precompute Features — DBN to .fxcache pipeline
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//!
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//! Reads OHLCV + MBP-10 + trades DBN data, runs the full feature extraction
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//! pipeline (standalone, no GPU required), and writes a `.fxcache` binary file for
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//! zero-overhead GPU loading during training.
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//!
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//! Usage:
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//! cargo run -p ml --example precompute_features --release -- \
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//! --data-dir test_data/futures-baseline \
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//! --mbp10-data-dir test_data/futures-baseline-mbp10 \
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//! --trades-data-dir test_data/futures-baseline-trades \
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//! --output-dir test_data/feature-cache \
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//! --symbol ES.FUT \
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//! --yes
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use std::path::{Path, PathBuf};
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use std::time::Instant;
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use anyhow::{Context, Result};
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use clap::Parser;
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use tracing::info;
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use ml::trainers::dqn::{
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collect_dbn_files_filtered, collect_dbn_files_recursive, extract_features_from_bars,
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};
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use ml::features::extraction::OHLCVBar;
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// ---------------------------------------------------------------------------
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// CLI
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// ---------------------------------------------------------------------------
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#[derive(Debug, Parser)]
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#[command(
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name = "precompute_features",
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about = "Precompute features from DBN data and write .fxcache binary"
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)]
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struct Opts {
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/// Directory containing OHLCV .dbn/.dbn.zst files (e.g. test_data/futures-baseline)
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#[arg(long)]
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data_dir: String,
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/// Directory containing MBP-10 .dbn/.dbn.zst files (optional)
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#[arg(long)]
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mbp10_data_dir: Option<String>,
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/// Directory containing trades .dbn/.dbn.zst files (optional)
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#[arg(long)]
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trades_data_dir: Option<String>,
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/// Output directory for .fxcache files (default: sibling of --data-dir)
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#[arg(long)]
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output_dir: Option<String>,
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/// Symbol name (used in output filename)
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#[arg(long, default_value = "ES.FUT")]
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symbol: String,
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/// Skip confirmation prompt
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#[arg(long)]
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yes: bool,
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}
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// ---------------------------------------------------------------------------
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// Cache key computation
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// ---------------------------------------------------------------------------
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/// Compute a SHA256 cache key from all DBN files across the given directories.
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///
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// ---------------------------------------------------------------------------
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// Main
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// ---------------------------------------------------------------------------
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#[tokio::main]
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async fn main() -> Result<()> {
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// Initialize tracing
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tracing_subscriber::fmt()
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.with_env_filter(
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tracing_subscriber::EnvFilter::try_from_default_env()
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.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
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)
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.init();
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let opts = Opts::parse();
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let data_dir = PathBuf::from(&opts.data_dir);
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// Default output_dir: sibling of data_dir (e.g. test_data/futures-baseline → test_data/feature-cache)
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let output_dir = match opts.output_dir {
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Some(ref d) => PathBuf::from(d),
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None => data_dir.parent().unwrap_or(&data_dir).join("feature-cache"),
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};
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let mbp10_dir = opts.mbp10_data_dir.as_ref().map(PathBuf::from);
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let trades_dir = opts.trades_data_dir.as_ref().map(PathBuf::from);
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// ── Validate inputs ──────────────────────────────────────────────────────
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if !data_dir.exists() {
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anyhow::bail!("Data directory not found: {}", data_dir.display());
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}
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// ── Print config summary ─────────────────────────────────────────────────
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println!("================================================================================");
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println!("Precompute Features — DBN to .fxcache Pipeline");
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println!("================================================================================");
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println!();
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println!("Data dir: {}", data_dir.display());
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if let Some(ref d) = mbp10_dir {
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println!("MBP-10 dir: {}", d.display());
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}
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if let Some(ref d) = trades_dir {
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println!("Trades dir: {}", d.display());
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}
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println!("Output dir: {}", output_dir.display());
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println!("Symbol: {}", opts.symbol);
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println!("Format: f32 (v{}), OFI_DIM={}", ml::fxcache::FXCACHE_VERSION, ml::fxcache::OFI_DIM);
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println!();
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// ── Confirmation ─────────────────────────────────────────────────────────
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if !opts.yes {
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print!("Proceed with feature extraction? (yes/no): ");
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std::io::Write::flush(&mut std::io::stdout())?;
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let mut input = String::new();
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std::io::stdin().read_line(&mut input)?;
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let trimmed = input.trim();
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if !trimmed.eq_ignore_ascii_case("yes") && !trimmed.eq_ignore_ascii_case("y") {
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println!("Cancelled.");
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return Ok(());
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}
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println!();
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}
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let t0 = Instant::now();
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const WARMUP: usize = 50;
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// ── Early exit if cache already exists for this data ─────────────────────
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let hex_key_early = ml::feature_cache::calculate_dbn_cache_key_full(
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&data_dir,
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mbp10_dir.as_deref(),
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trades_dir.as_deref(),
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&opts.symbol,
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"ohlcv",
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).context("Failed to compute cache key")?;
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let early_check_path = output_dir.join(format!("{hex_key_early}.fxcache"));
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if early_check_path.exists() {
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let size_mb = std::fs::metadata(&early_check_path)
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.map(|m| m.len() as f64 / 1_048_576.0)
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.unwrap_or(0.0);
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println!("Cache already exists: {} ({:.1} MB) — skipping extraction",
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early_check_path.display(), size_mb);
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return Ok(());
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}
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// ── Step 1: Build volume bars from trades data ────────────────────────────
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let t1 = Instant::now();
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// Use trades_data_dir if provided, otherwise try data_dir for trades
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let trades_source = trades_dir.as_ref().unwrap_or(&data_dir);
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info!("Loading trades from {} (symbol={})...", trades_source.display(), opts.symbol);
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let mut trade_files = collect_dbn_files_filtered(trades_source, Some(&opts.symbol));
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if trade_files.is_empty() {
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// Fallback: try data_dir if trades_dir was specified but had no files
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if trades_dir.is_some() {
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trade_files = collect_dbn_files_filtered(&data_dir, Some(&opts.symbol));
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}
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if trade_files.is_empty() {
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anyhow::bail!(
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"No trades DBN files found for symbol '{}' in {} or {}",
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opts.symbol,
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trades_source.display(),
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data_dir.display()
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);
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}
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}
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trade_files.sort();
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info!("Found {} trades/OHLCV DBN files for symbol '{}'", trade_files.len(), opts.symbol);
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// Load trades PER FILE and filter front-month within each file.
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// Each quarterly DBN file has a different front-month contract (e.g. ESH24, ESM24).
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// Filtering globally would select only ONE quarter's contract.
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let mut all_front_month_trades: Vec<ml::features::DbnTrade> = Vec::new();
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let mut total_raw = 0_usize;
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for file in &trade_files {
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match ml::features::load_trades_sync(file) {
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Ok(trades) => {
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let raw_count = trades.len();
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total_raw += raw_count;
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let filtered = ml::features::filter_front_month(&trades);
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info!(" {} -> {} trades, {} front-month",
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file.file_name().unwrap_or_default().to_string_lossy(),
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raw_count, filtered.len());
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all_front_month_trades.extend(filtered);
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}
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Err(e) => {
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tracing::warn!(" Failed {:?}: {e}", file.file_name());
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}
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}
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}
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all_front_month_trades.sort_by_key(|t| t.timestamp);
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info!("Total trades: {} raw, {} front-month", total_raw, all_front_month_trades.len());
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// Build volume bars
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let all_bars = ml::features::build_volume_bars(&all_front_month_trades, ml::features::DEFAULT_VOLUME_BAR_SIZE);
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info!("Volume bars built: {} bars ({} contracts/bar) in {:.1}s",
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all_bars.len(), ml::features::DEFAULT_VOLUME_BAR_SIZE, t1.elapsed().as_secs_f64());
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if all_bars.len() < WARMUP + 2 {
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anyhow::bail!(
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"Insufficient data: {} volume bars, need at least {} (WARMUP={WARMUP} + 2)",
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all_bars.len(), WARMUP + 2
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);
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}
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// ── Price continuity check (regression gate) ────────────────────────
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let mut max_jump_pct = 0.0_f64;
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let mut jump_count = 0_usize;
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for w in all_bars.windows(2) {
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let pct = ((w[1].close - w[0].close) / w[0].close).abs() * 100.0;
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if pct > 0.5 {
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jump_count += 1;
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}
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max_jump_pct = max_jump_pct.max(pct);
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}
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info!("Price continuity: max_jump={:.3}%, jumps>0.5%={}/{}", max_jump_pct, jump_count, all_bars.len() - 1);
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if jump_count as f64 / all_bars.len() as f64 > 0.01 {
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anyhow::bail!(
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"PRICE CONTINUITY FAILED: {} of {} bars have >0.5% price jump — likely interleaved contracts",
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jump_count, all_bars.len()
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);
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}
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// ── Step 2: Extract 42-dim feature vectors ──────────────────────────────
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let t1 = Instant::now();
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info!("Extracting features...");
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let feature_vectors = extract_features_from_bars(&all_bars)
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.context("Feature extraction failed")?;
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info!("Extracted {} feature vectors in {:.1}s", feature_vectors.len(), t1.elapsed().as_secs_f64());
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// ── Step 3: Build targets [preproc_close, preproc_next, raw_close, raw_next]
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let n = feature_vectors.len().saturating_sub(1); // need next bar for target
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let features: Vec<[f64; 42]> = feature_vectors[..n].to_vec();
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let mut targets: Vec<[f64; 4]> = Vec::with_capacity(n);
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for i in 0..n {
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let raw_curr = all_bars[i + WARMUP].close;
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let raw_next = all_bars[i + WARMUP + 1].close;
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targets.push([raw_curr, raw_next, raw_curr, raw_next]);
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}
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// ── Step 3b: Build per-bar timestamps ─────────────────────────────────
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let timestamps: Vec<i64> = (0..n)
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.map(|i| all_bars[i + WARMUP].timestamp.timestamp_nanos_opt().unwrap_or(0))
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.collect();
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// ── Step 4: Compute OFI from MBP-10 + trades ────────────────────────────
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let t2 = Instant::now();
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let ofi: Vec<[f64; 20]> = if let Some(ref mbp10_path) = mbp10_dir {
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use ml::features::mbp10_loader::load_ofi_features_parallel;
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use ml::features::trades_loader::load_trades_sync;
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use ml::features::ofi_calculator::OFICalculator;
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info!("Loading MBP-10 snapshots from {}...", mbp10_path.display());
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let mut mbp10_files = collect_dbn_files_recursive(mbp10_path);
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mbp10_files.sort();
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if mbp10_files.is_empty() {
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info!("No MBP-10 files found, OFI will be zeros");
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vec![[0.0; 20]; n]
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} else {
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// Load MBP-10 snapshots (parallel)
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use data::providers::databento::dbn_parser::DbnParser;
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let per_file: Vec<Vec<_>> = {
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use rayon::prelude::*;
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mbp10_files.par_iter().filter_map(|file| {
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let parser = match DbnParser::new() {
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Ok(p) => p,
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Err(e) => { tracing::warn!("Parser init failed: {e}"); return None; }
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};
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let mut snapshots = Vec::new();
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match parser.parse_mbp10_streaming(file, 100, |snap| {
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snapshots.push(snap.clone());
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}) {
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Ok(_) => {
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info!(" {} -> {} snapshots", file.file_name().unwrap_or_default().to_string_lossy(), snapshots.len());
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Some(snapshots)
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}
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Err(e) => {
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tracing::warn!(" Failed {:?}: {e}", file.file_name());
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None
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}
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}
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}).collect()
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};
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let mut all_snapshots = Vec::new();
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for snaps in per_file { all_snapshots.extend(snaps); }
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all_snapshots.sort_by_key(|s| s.timestamp);
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info!("Loaded {} MBP-10 snapshots in {:.1}s", all_snapshots.len(), t2.elapsed().as_secs_f64());
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// Load trades (parallel)
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let all_trades = if let Some(ref tdir) = trades_dir {
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let mut trade_files = collect_dbn_files_recursive(tdir);
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trade_files.sort();
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let per_file_trades: Vec<Vec<_>> = {
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use rayon::prelude::*;
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trade_files.par_iter().filter_map(|path| {
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match load_trades_sync(path) {
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Ok(t) => { info!(" {} trades from {:?}", t.len(), path.file_name()); Some(t) }
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Err(e) => { tracing::warn!(" Failed {:?}: {e}", path.file_name()); None }
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}
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}).collect()
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};
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let mut trades = Vec::new();
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for t in per_file_trades { trades.extend(t); }
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trades.sort_by_key(|t| t.timestamp);
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if trades.is_empty() { None } else { Some(trades) }
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} else {
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None
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};
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// Compute per-bar OFI
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use ml::features::trades_loader::get_trades_for_bar;
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use ml::features::ofi_calculator::MicrostructureState;
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let mut calculator = OFICalculator::new();
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let mut ofi_per_bar = Vec::with_capacity(n);
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for i in 0..n {
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let bar = &all_bars[i + WARMUP];
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let bar_ts = bar.timestamp.timestamp_nanos_opt().unwrap_or(0) as u64;
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let bar_end_ts = all_bars.get(i + WARMUP + 1)
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.map(|b| b.timestamp.timestamp_nanos_opt().unwrap_or(0) as u64)
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.unwrap_or(bar_ts + 60_000_000_000);
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let bar_duration_ns = bar_end_ts - bar_ts;
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let mut micro_state = MicrostructureState::new(bar_ts, bar_duration_ns);
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// Feed trades for this bar window (OFICalculator + MicrostructureState)
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if let Some(ref trades) = all_trades {
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for trade in get_trades_for_bar(trades, bar_ts, bar_end_ts) {
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calculator.feed_trade(trade.price, trade.volume, trade.is_buy);
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micro_state.update_trade(trade.price, trade.volume, trade.is_buy, trade.timestamp);
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}
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}
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// Get ALL MBP-10 snapshots within this bar window for tick-level features.
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// Binary search for bar range: [bar_ts, bar_end_ts)
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let snap_start = all_snapshots.partition_point(|s| s.timestamp < bar_ts);
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let snap_end = all_snapshots.partition_point(|s| s.timestamp < bar_end_ts);
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let bar_snapshots = &all_snapshots[snap_start..snap_end];
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// Feed every snapshot to MicrostructureState for tick-level resolution
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for snap in bar_snapshots {
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micro_state.update_snapshot(snap);
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}
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// Use the LAST snapshot in the bar for OFI calculation (matches prior behavior)
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let last_snap = bar_snapshots.last()
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.or_else(|| {
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// Fallback: nearest snapshot at/after bar_ts (prior behavior)
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all_snapshots.get(snap_start)
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});
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if let Some(snap) = last_snap {
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match calculator.calculate(snap) {
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Ok(f) if f.is_valid() => {
|
||
// Feed OFI-derived values to MicrostructureState
|
||
micro_state.update_ofi_derived(f.ofi_level1, f.vpin);
|
||
|
||
let arr8 = f.to_array();
|
||
let micro_12 = micro_state.snapshot();
|
||
let mut arr20 = [0.0_f64; 20];
|
||
arr20[..8].copy_from_slice(&arr8);
|
||
arr20[8..20].copy_from_slice(µ_12);
|
||
ofi_per_bar.push(arr20);
|
||
}
|
||
_ => ofi_per_bar.push([0.0; 20]),
|
||
}
|
||
} else {
|
||
ofi_per_bar.push([0.0; 20]);
|
||
}
|
||
}
|
||
|
||
let non_zero = ofi_per_bar.iter().filter(|f| f.iter().any(|&v| v != 0.0)).count();
|
||
info!("OFI computed: {} bars, {} non-zero ({:.1}%) in {:.1}s",
|
||
ofi_per_bar.len(), non_zero,
|
||
if ofi_per_bar.is_empty() { 0.0 } else { non_zero as f64 / ofi_per_bar.len() as f64 * 100.0 },
|
||
t2.elapsed().as_secs_f64());
|
||
ofi_per_bar
|
||
}
|
||
} else {
|
||
info!("No MBP-10 directory, OFI will be zeros");
|
||
vec![[0.0; 20]; n]
|
||
};
|
||
|
||
let total_len = n;
|
||
|
||
// ── Compute cache key ────────────────────────────────────────────────────
|
||
let hex_key = ml::feature_cache::calculate_dbn_cache_key_full(
|
||
&data_dir,
|
||
mbp10_dir.as_deref(),
|
||
trades_dir.as_deref(),
|
||
&opts.symbol,
|
||
"ohlcv",
|
||
).context("Failed to compute cache key")?;
|
||
let cache_key: [u8; 32] = hex::decode(&hex_key)
|
||
.context("Invalid hex key")?
|
||
.try_into()
|
||
.map_err(|_| anyhow::anyhow!("Cache key wrong length"))?;
|
||
|
||
// ── Normalize features (z-score) ──────────────────────────────────────────
|
||
// Raw features contain OHLCV prices (up to 25,000). Normalize once at
|
||
// precompute time so every consumer
|
||
// (training, hyperopt, inference) gets consistent normalized features.
|
||
let norm_stats = ml::walk_forward::NormStats::from_features(&features);
|
||
let features = norm_stats.normalize_batch(&features);
|
||
info!("Features z-score normalized ({} bars × 42 dims)", features.len());
|
||
|
||
// ── Write .fxcache ───────────────────────────────────────────────────────
|
||
std::fs::create_dir_all(&output_dir)
|
||
.with_context(|| format!("Failed to create output directory: {}", output_dir.display()))?;
|
||
|
||
let output_path = output_dir.join(format!("{hex_key}.fxcache"));
|
||
|
||
// Save NormStats alongside cache for inference denormalization
|
||
let norm_path = output_dir.join(format!("{hex_key}.norm_stats.json"));
|
||
let norm_json = serde_json::to_string_pretty(&norm_stats)
|
||
.context("Failed to serialize NormStats")?;
|
||
std::fs::write(&norm_path, &norm_json)
|
||
.with_context(|| format!("Failed to write NormStats: {}", norm_path.display()))?;
|
||
info!("NormStats saved to {}", norm_path.display());
|
||
|
||
let t2 = Instant::now();
|
||
info!("Writing .fxcache to {}...", output_path.display());
|
||
|
||
let has_ofi = mbp10_dir.is_some();
|
||
let bytes_written = ml::fxcache::write_fxcache(
|
||
&output_path,
|
||
&features,
|
||
&targets,
|
||
&ofi,
|
||
×tamps,
|
||
cache_key,
|
||
has_ofi,
|
||
)
|
||
.context("Failed to write .fxcache file")?;
|
||
|
||
let write_secs = t2.elapsed().as_secs_f64();
|
||
let total_secs = t0.elapsed().as_secs_f64();
|
||
|
||
// ── Summary ──────────────────────────────────────────────────────────────
|
||
println!();
|
||
println!("================================================================================");
|
||
println!("PRECOMPUTE SUMMARY");
|
||
println!("================================================================================");
|
||
println!();
|
||
println!("Bars: {}", total_len);
|
||
println!("Features: 42-dim");
|
||
println!("Targets: 4-dim");
|
||
println!("OFI: 20-dim ({})", if has_ofi { "from MBP-10" } else { "zero-padded" });
|
||
println!("Format: f32 (v{}), OFI_DIM={}", ml::fxcache::FXCACHE_VERSION, ml::fxcache::OFI_DIM);
|
||
println!("Cache key: {}", hex_key);
|
||
println!("Output: {}", output_path.display());
|
||
println!("Size: {:.2} MB", bytes_written as f64 / 1_048_576.0);
|
||
println!("Write time: {:.1}s", write_secs);
|
||
println!("Total time: {:.1}s", total_secs);
|
||
println!();
|
||
|
||
Ok(())
|
||
}
|