fd415d9b17
Systematic completion of the per_α fix (commit 0857d40ac) across the
two other additive-target controllers that had the same dead-zone
anti-pattern: hardcoded bootstrap value that coincides with a target
the formula naturally produces.
## Audit
Across the 7 RL controllers in `crates/ml-alpha/cuda/rl_*_controller.cu`,
target formulas split into three classes:
1. **Additive target** (target = f(input)): `per_α`, `γ`, `coef`.
Hardcoded bootstrap can collide with target value at specific
input — the dead-zone fix applies cleanly via derive-from-input.
2. **Multiplicative target** (target = prev × ratio): `τ`, `ε`,
`n_roll`. Bootstrap IS the initial `prev`; the "no movement when
ratio=1" case corresponds to "input at steady-state value" which
is correct behavior, not a dead-zone bug. Not changed.
3. **Special** (target = 1/input): `scale`. Sentinel input → 1/0
would blow up. Hardcoded bootstrap 1.0 + production input range
(mean_abs_pnl ≫ 1.0 for ES futures) means no practical dead-zone.
Not changed.
## γ kernel fix
Hardcoded `GAMMA_BOOTSTRAP = 0.99` coincided with `target(d ≈ 69)`
via `γ = 0.5^(1/d)`. For canonical hold times near 69 events (which
is exactly the d at which γ=0.99 is correct), the Wiener blend
`prev=0.99, target=0.99` produced no movement.
Now: bootstrap = `clamp(0.5^(1/max(d, 1)), GAMMA_MIN, GAMMA_MAX)`.
At sentinel input (d=0 → clamped d=1) → target=0.5 → clamped to
GAMMA_MIN = 0.90 (the floor). Cold-start γ is the floor (more
myopic for first few steps); as `trade_duration_ema` stabilises in
the typical 10-100 range, the controller drifts γ up toward
`0.5^(1/d_observed)`.
## coef kernel fix
Hardcoded `COEF_BOOTSTRAP = 0.01` coincided with `target(h_obs ≈
1.099)` via `coef = (h_target - h_obs) / h_max × COEF_MAX`. For
mid-range observed entropy (≈ half of h_target ≈ 1.538), bootstrap
= target → frozen.
Now: bootstrap = `(h_target - max(h_obs, 0)) / h_max × COEF_MAX`,
clamped. At sentinel input (h_obs=0) → deficit = h_target = 1.538
→ target = (1.538 / 2.197) × 0.05 ≈ 0.035 (3.5× the previous
canonical 0.01). Lifts the entropy bonus's relative weight in early
training — desirable cold-start behavior (push exploration when no
entropy data yet) and self-corrects as `entropy_observed_ema`
stabilises.
## Test updates
* `isv_bootstrap.rs`: `GAMMA_BOOTSTRAP` 0.99 → 0.90, `COEF_BOOTSTRAP`
0.01 → 0.035. Inline comments document the post-R9-audit derive-
from-input rationale.
* `r5_controllers_and_soft_update.rs`: same constants updated;
`trade_duration_ema` fixture input 1.0 → 20.0 because d=1 produces
target=0.5 which clamps to GAMMA_MIN = 0.90 = new bootstrap = floor
(canonical "all production-realistic trade durations are 10-100
events" range). The d=1 fixture was an unrealistic edge case
(sub-event trade duration is non-physical).
* `r3_ema_advantage.rs::r3_compute_advantage_return_formula_holds`:
pre-condition assertion loosened from `γ == 0.99` to `γ ∈ [0.90,
0.999]` — the test computes its expected values from whatever γ
ISV holds, so the hardcoded comparison was incidental.
* `trainer/integrated.rs` `with_controllers_bootstrapped` docstring:
γ and coef slot docs updated to reflect derive-from-input.
## Verified gates (post-fix, local sm_86)
G1 isv_bootstrap ✅ γ=0.90 τ=0.005 ε=0.2 coef=0.035
n_roll=2048 per_α=0.4 scale=1.0
G3 controllers_emit ✅ γ 0.9 → 0.926 (target 0.966)
coef 0.035 → 0.030 (target 0.025
at h_obs=0.5)
G4 target_soft_update ✅ unchanged
G6 r7d_per_wiring ✅ unchanged
R3 ema/advantage (3 tests) ✅ pre-cond loosened, formula intact
R4 action kernels (3 tests) ✅ unchanged
end integrated_trainer_smoke ✅ all 5 head losses finite
## What's NOT in this commit
Multiplicative controllers (τ, ε, n_roll) and the special-form scale
controller still use hardcoded bootstraps. Their dead-zones (if any)
are either correct steady-state behavior (multiplicative) or
practically unreachable (scale's dead-zone at mean_abs_pnl=1.0 is
not hit by ES dollar-scale rewards). Documenting these as
"intentionally hardcoded" is preferable to forcing derive-from-input
where it doesn't naturally fit.
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.