ed6f5588e15be7f823b88cf5b26279a30bd132f2
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>
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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
Languages
Rust
88.2%
Cuda
7.7%
Python
1.3%
Shell
1.1%
PLpgSQL
0.8%
Other
0.8%