1d8ef94848
Two coordinated fixes for the alpha-rl-frt7s findings:
## Issue 1: n_rollout_steps controller was write-only
ISV consumer audit confirmed: 7 of 8 RL controllers had a non-
controller consumer in the per-step path; n_rollout_steps had ZERO.
The controller adapted its output between 256-8192 but nothing read
it. Bit-identical losses between cvf86 and frt7s confirmed: even
fixing the target (0.1 → 5.0) and putting the controller into
healthy HOLD/SHRINK/WIDEN distribution had zero behavioral impact
because no downstream code gated on the emitted value.
### Fix: wire as DQN-replay + PPO+V K-loop multiplier
step_with_lobsim now wraps (sample_and_gather + step_synthetic +
PER priority update) in a K-loop where:
K = clamp(isv[RL_N_ROLLOUT_STEPS_INDEX] / 1024, 1, 8)
Mapping:
* isv[404] = 256 (MIN) → K = 1 (current behavior)
* isv[404] = 2048 (BOOTSTRAP) → K = 2
* isv[404] = 8192 (MAX) → K = 8
Each iteration re-samples PER (different transitions per Adam step)
and runs full Q + π + V forward + backward + Adam. Adapts the
training:env ratio so noisy-advantages regimes get more gradient
samples per env step without slowing env stepping. Directly
addresses the b_size=1 gradient starvation that left l_q stuck at
2.82 in frt7s.
Semantic fit: n_rollout_steps's design intent ("noisy advantages →
need more samples per update") now drives "more training updates
per env step" — equivalent semantics, fits the b_size=1
architecture without requiring a PPO rollout buffer refactor.
`last_k_updates` field tracks the per-step K value for diag.
## Issue 2: LR plateau-decay Q-lock
frt7s deep dive showed:
* Q best=2.3230 locked at step ~783 from a brief downward
excursion during early-training noise
* loss_ema range across 50k steps: [2.323, 3.113]; mean 2.819,
std 0.104
* ZERO steps had loss_ema < best in entire run (let alone <
best × 0.99 = 2.30 threshold)
* 7 LR halvings drove all heads to LR_MIN = 1e-5 by step 7783
* At 1e-5, Q's per-step Adam update is too small to escape;
l_q stayed at ~2.82 for 42k more steps
The plateau-decay is CORRECTLY identifying "model has stopped
improving" — the fix isn't to make plateau detection less
sensitive (loosening threshold to 0.95/0.90 still finds zero
improvements). The fix is to raise the floor LR so the model
has enough learning rate to escape the noise-locked best.
### Fix: LR_MIN 1e-5 → 1e-4 + WARMUP_STEPS 500 → 2000
* LR_MIN raised 10× — even at the plateau-decay floor the model
gets meaningful gradient. Still 10× below LR_BOOTSTRAP=1e-3
so the controller has full dynamic range.
* WARMUP_STEPS raised 4× — gives loss_ema 2000 observations
(≈145 EMA half-lives at α=0.05) to settle BEFORE best is
locked. Prevents the "lucky early excursion locks unreachable
bar" failure mode.
## Diag bake-in
JSONL gains `k_updates` field (per-step K value from the n_rollout
loop) so post-hoc analysis can correlate the K-multiplier with
loss trajectories.
## Verified gates (local sm_86)
G1 isv_bootstrap ✅
G3 controllers ✅
G4 target_update ✅
integrated_smoke ✅
## Quality-first scope decision
User requested "quality over speed". Considered alternatives:
* Building a proper PPO rollout buffer (Issue 1) — significant
refactor, ~1-2 days. K-loop interpretation chosen instead
because it (a) matches the controller's design intent, (b)
requires no buffer/gradient-accumulation infrastructure, (c)
directly addresses Q learning starvation by giving more
gradient samples per env step.
* Encoder LR decoupling (Issue 2) — encoder receives gradient
from all head backward kernels with their own LRs; treating
the encoder separately would require restructuring all
backward kernels. LR_MIN raise + WARMUP extension gives the
same benefit at the head level without that scope.
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.