a01a376bd2
Per `pearl_q_thompson_actor_makes_pi_dead_weight` follow-up + #35
deferral: the K-loop in step_with_lobsim was running full
step_synthetic K times per env step (Q + π + V + encoder + LR
controller emit + ISV refresh + EMA inputs). At K=4 (default) or K=8
(prior K_MAX) this caused PPO overshoot — KL excursions to 12.44 in
f2ggr, policy drift faster than the env step rate, gradient
overtraining on the same env-step's h_t.
## Fix: extract dqn_replay_step helper
New public method `dqn_replay_step(b_size)`:
1. Forward Q on sampled_h_t + sampled_h_tp1 (Double-DQN argmax)
2. Bellman target via TARGET net at h_tp1 + select + project
3. Q backward (logits → grad_w/b/h_t)
4. Per-batch reduce → grad_w/grad_b
5. Q Adam (uses LR already set by step_synthetic — no re-fire of
the LR controller per K iter)
6. Writes td_per_sample_d for PER priority update by caller
Discards Q's grad_h_t per R7d stop-grad (same as step_synthetic).
What dqn_replay_step does NOT do:
* π forward / surrogate / Adam — runs once per env step in
step_synthetic
* V forward / backward / Adam — same
* Encoder backward / grad combine — same
* LR controller emit + ISV mirror refresh — same
* EMA inputs (entropy, KL, advantage_var, td_kurtosis) — same
## K-loop in step_with_lobsim
for k_iter in 0..k_updates {
let per_indices = sample_and_gather(b_size)?;
if k_iter == 0 {
stats = step_synthetic(snapshots)?; // full update
} else {
dqn_replay_step(b_size)?; // Q-only
}
// PER priority update
}
Result:
* Q gets K Adam updates per env step (K-fold variance reduction)
* π + V + encoder get 1 Adam update per env step (no overshoot)
* LR controllers fire once per env step (no double-counting of
plateau detection)
* At b_size=16 with low advantage_var_ratio (batch averaging
reduces noise), K-loop typically settles at K=1 — the split
becomes a no-op in the steady state. At b_size=1 fallback or
high-noise regimes, the split materially reduces PPO drift.
## Code duplication
dqn_replay_step duplicates ~120 lines of Q-section code from
step_synthetic. Acceptable temporary tech debt — full dedupe would
require restructuring step_synthetic to call dqn_replay_step
internally, which is a larger refactor with regression risk. Marked
TODO for a follow-up commit once the b_size=16 + π-actor + K-split
architecture is empirically validated.
## Verified gates (local sm_86)
G1 isv_bootstrap ✅
G3 controllers ✅
G4 target_update ✅
integrated_smoke ✅
## No smoke yet
alpha-rl-9k9x6 (commit 3737feb66, π-actor + b_size=16) is still in
flight; submitting a new smoke would compete for L40S GPU. This
commit lands on remote; smoke will be submitted after 9k9x6 lands
and we've analyzed whether the b_size=16 + π-actor architecture
worked. If 9k9x6 shows Q learning unblocked, this split is polish.
If it doesn't, the split becomes the next experiment.
Co-Authored-By: Claude Opus 4.7 <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.