acde2e8932
Closes the long-standing "h_t as proxy for s_{t+1}'s encoder
representation" approximation introduced in Phase E.2's Bellman target
build (canonical comment at the call site: "A future enhancement
(Phase E.3 LobSim integration) will pass next_h_t separately"). The
approximation also leaked into compute_advantage_return's V(s_{t+1})
input — R7b's `v_tp1_d_ref = &v_pred_d` alias — and into R4's
argmax_expected_q kernel call, which had been computing the
Double-DQN argmax on online Q at h_t since R4 first wired it.
Three downstream consumers now read TRUE h_{t+1}:
* `value_head.forward(&self.h_tp1_d) → v_pred_tp1_d`, fed to
`compute_advantage_return` as the canonical TD target V(s_{t+1}).
Was an alias of v_pred_d (V(s_t)) — bootstrap was wrong by one
time index.
* `dqn_head.forward(&self.h_tp1_d) → q_logits_tp1_d`, fed to
`argmax_expected_q` for `next_actions_d` (Double-DQN online-Q
argmax on h_{t+1}, not h_t).
* `dqn_head.forward_target(&self.h_tp1_d)` inside step_synthetic's
Bellman target build. Replaces the h_t-as-proxy comment with the
R7c data-correctness lift inline-doc.
Wiring mechanics:
* New trainer field `h_tp1_d: CudaSlice<f32>` (`[B × HIDDEN_DIM]`).
Zero-initialised; populated each step by step_with_lobsim.
* `step_with_lobsim` signature gains a `next_snapshots:
&[Mbp10RawInput]` parameter (caller — R8's CLI binary — uses
`MultiHorizonLoader::next_sequence_pair` from R2 to load adjacent
`(s_t, s_{t+1})` windows from real MBP-10 data).
* Encoder is now called TWICE in step_with_lobsim:
1. `forward_encoder(next_snapshots)` first → DtoD copy
`perception.h_t_d → self.h_tp1_d` immediately (before any
consumer reads the slot).
2. `forward_encoder(snapshots)` second → leaves perception's
internal forward state (`h_new_per_k_d`, CfC `h_state_d`)
primed for step_synthetic's encoder backward (which still
redundantly re-runs `forward_encoder(snapshots)` per the
pre-existing pattern — separate compute-redundancy fuse for
Phase R-future).
* Three encoder forwards total per step (down from R7b's 2: one
in step_with_lobsim, one in step_synthetic — R7c adds the
second-snapshot forward in step_with_lobsim). The CfC encoder is
deterministic given its input window (per the canonical
step_with_lobsim header comment), so calling forward_encoder
twice on different inputs in a row yields independent h_t and
h_{t+1} via the trainer's own DtoD copy.
Test impact:
* `integrated_trainer_smoke.rs` passes a `next_snapshots` second
window (synthesised as a +1-tick shift of the snapshot window
— production callers will use R2's `next_sequence_pair` on real
MBP-10 data). Smoke continues to assert finite losses across the
five heads.
Scope split note: this commit handles ONLY the data-correctness
half of plan A9 (rebuild plan's "PER buffer + true V(s_{t+1})
Bellman target" R7 scope). The off-policy DQN-via-PER half lands
in the next commit (R7d): Replay-buffer push, sample,
stop-grad-on-encoder Q redirect, and per-sample |TD| → update_priorities.
Split is per `pearl_no_deferrals_for_complementary_fixes`'s
sequencing-with-architectural-justification carve-out: R7d's PER
push requires the correct h_{t+1} this commit provides, so it MUST
sequence after — and the data-correctness fix is independently
useful (the on-policy DQN path now produces correct Bellman
targets even without the off-policy replay).
Local sm_86 smoke: `cargo test -p ml-alpha --test
integrated_trainer_smoke -- --ignored --nocapture` is the gate.
Cluster smoke deferred to R9.
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.