82dca76dae
Adds `ig_diag` binary under ml-explainability/src/bin/ that runs
Integrated Gradients on a trained DQN safetensors checkpoint and
writes a per-feature attribution report as JSON. Designed for offline
model inspection, not the inference hot path.
Forward target: mean(Q[direction, 0..4]), which simplifies to V(s)
under the dueling identity (mean of centered advantages is zero by
the identifiability constraint). Smooth, no argmax discontinuity,
well-posed for IG.
Regime-head handling: loads only the `trending__`-prefixed weights
(matches RegimeConditionalDQN::load_from_merged_safetensors). Full
multi-head attribution is a future extension.
CLI args:
--checkpoint PATH safetensors file
--states auto|PATH `auto` samples from test_data/feature-cache/
*.fxcache; otherwise a JSON file containing
{ "states": [[f32; STATE_DIM], ...] }
--feature-names PATH JSON array of STATE_DIM names (optional)
--num-steps N IG Riemann steps (default 50)
--output PATH output JSON (default ig_report.json)
--auto-samples N fxcache sample count (default 16)
--seed N LCG seed for reproducibility (default 42)
Output JSON schema:
{
"schema_version": 1,
"checkpoint", "num_steps", "state_dim", "num_states",
"forward_target", "regime_head",
"features": [ { "name", "mean_abs", "stddev", "mean_signed" } ],
"top_10_by_mean_abs": [...],
"completeness": {
"worst_relative_error": f64,
"per_state": [ { "state_idx", "sum_attributions",
"f_input_minus_f_baseline", "relative_error" } ]
}
}
NoisyNet is disabled on both the Q-network and target network before
IG runs so attributions are deterministic (same checkpoint + states +
num_steps = bit-identical attributions).
Gated behind the `ig-diag-cli` Cargo feature (optional Cargo binary
feature, not a runtime flag) because the binary depends on ml-dqn.
Cannot depend on `ml` due to a cyclic dependency (ml already depends
on ml-explainability). To avoid pulling in the heavy `ml` crate, the
fxcache header parser is reimplemented inline in the binary (~80 LOC,
matches ml/src/fxcache.rs byte-for-byte for v4 files).
Tests:
- tests/ig_diag_cli_integration.rs: end-to-end test that builds a
DQN, saves a checkpoint, writes a states JSON, invokes the binary
via std::process::Command, parses the output, asserts schema +
completeness axiom (worst relative error < 5%). Gated with
#[cfg(all(feature = "cuda", feature = "ig-diag-cli"))] + #[ignore]
because it needs CUDA + the binary pre-built.
Build/run:
cargo build --release -p ml-explainability \
--features ig-diag-cli --bin ig_diag
cargo test -p ml-explainability --features ig-diag-cli \
--test ig_diag_cli_integration -- --ignored --nocapture
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.