jgrusewski ed6f5588e1 feat(phase-e-4-a): wire Mamba2 forward in smoke --temporal path
Phase E.4.A Task 8: wire ml_alpha::Mamba2Block as the temporal
encoder before the C51 head when --temporal is set.

Architecture (--temporal):
  state_pinned ──push─▶ window_tensor[1, K=16, in_dim=10]
                              │
                              ▼ Mamba2Block::forward_train
                       h_enriched[1, hidden_dim=32]
                              │
                              ▼ launch_alpha_c51_forward (input dim=32)
                       probs[1, 9, 51] ──▶ Thompson selector

Implementation:
- Mamba2Block constructed at startup with config (in_dim=10,
  hidden_dim=32, state_dim=16, seq_len=K=16). Loaded from ml-alpha's
  precompiled cubin.
- Per-step: window push (shift+insert), then forward_train returns
  (logit, cache). We discard logit (ml-alpha's binary classifier head)
  and use cache.h_enriched as the C51 input.
- Per-step h_enriched cached into h_enriched_buf_dev[(t)..t+hidden_dim].
- Batched training (end-of-episode): the C51 forward + grad use
  h_enriched_buf_dev[0..ep_len*hidden] for the current state and
  [hidden..(ep_len+1)*hidden] for next-state (1-step offset). Runs
  one extra Mamba2 forward on the terminal window to populate slot
  ep_len.
- C51 input dim (W shape) becomes mamba2_hidden_dim when --temporal,
  STATE_DIM otherwise.

100-episode smoke verdict (vs C51-flat baseline):
  R_mean ep 50:  C51-flat -8.2  →  --temporal +0.4   (+8.6)
  R_mean ep 100: C51-flat +0.5  →  --temporal +10.0  (+9.5)
  rvr:           +1.045 → +1.046 (unchanged)
  Q_SPREAD:      23.9 → 12.6 (sharper distributions)
  ACTION_ENTROPY: 1.42 → 1.49 (now PASSES 0.5×ln(9) threshold)

Note: Mamba2 weights are FROZEN at random Xavier init in this
commit — T10 (backward + AdamW step) lands next. The R_mean lift
above is from C51 learning over RANDOM temporal projections of the
window — random SSM acts as a feature-engineering reservoir.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-15 21:14:31 +02:00

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.

Description
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