c16b3b5a80
Phase 1.3.b-followup (5b394f103) landed the main per-env redesign but
deferred dd_trajectory + per_insert_pa migration as Followup-B because
the contract shape is per-(env, t) buffer [N*L], not per-env tile.
This commit:
- Reshapes dd_trajectory_decreasing_kernel to per-env grid
[n_envs, 1, 1] x [1, 1, 1]; writes dd_trajectory_per_env[env]
- Migrates per_insert_pa to per-transition lookup via
env_id = (j % (n_envs * lookback)) / lookback; reads
dd_trajectory_per_env[env_id]
- Allocates two collector-owned per-env tiles
(sp15_dd_trajectory_per_env + sp15_dd_trajectory_prev_dd_per_env);
deletes the trainer-owned [1] sp15_dd_trajectory_prev_dd scratch +
its setter wiring (replaced by unconditional collector ownership,
mirrors sp15_dd_state_per_env pattern)
- Extends dd_state_reduce_kernel to mean-aggregate per-env
trajectory tile -> ISV[DD_TRAJECTORY_DECREASING_INDEX=439] for
HEALTH_DIAG diagnostic preservation
- Wires per-env tile dev_ptr + (n_envs, lookback) dims into
GpuReplayBuffer via new setters (set_sp15_dd_trajectory_per_env_ptr,
set_sp15_per_env_dims) called from training_loop
- Migrates 4 dd_trajectory + 1 PER oracle tests to per-env contract
- Adds NEW behavioral test
per_sampler_weights_per_transition_not_uniform_across_batch:
n_envs=2 batch with env-0 trajectory=1, env-1 trajectory=0; asserts
priorities[env-0 slots]=3.0 and priorities[env-1 slots]=1.0 in the
SAME insert batch (Wave 4.3 would produce uniform 3.0 OR uniform
1.0 across all 8 priorities — the test directly fails the old
implementation)
- Layout fingerprint marker DD_TRAJECTORY_PER_ENV=sp15_phase_1_3_b_followup_B
(greenfield checkpoints OK per spec Q1)
Fixes the Wave 4.3 PER limitation: ISV[DD_TRAJECTORY_DECREASING_INDEX=
439] was read ONCE per insert call and applied uniformly to ALL
n_envs * lookback * 2 transitions in the batch, so the recovery
oversample was statistically biased (whichever env wrote ISV[439]
most recently determined the boost for ALL inserted transitions).
Per-transition lookup fixes this — each transition's weight reflects
its own env's recovery context.
Atomic per feedback_no_partial_refactor: kernel reshape + 2 collector-
owned per-env tiles + trainer struct cleanup + reduction kernel
extension + per_insert_pa kernel signature change + 3 new GPU PER
replay buffer fields/setters/accessors + per_insert_pa launch site
update + collector launch sequence update + state-reset registry
rename (1->2 entries) + 2 dispatch arms + training_loop wiring
delete/replace + 4 dd_trajectory test migrations + 1 PER test
migration + 1 NEW behavioral test + 2 dd_state_reduce callsite
null updates + audit doc all in this commit.
Closes the per-env DD redesign chain end-to-end. SP15 reward shaping
+ recovery-curriculum PER oversample now correctly apply per-env
context everywhere they're consumed; no more "whichever env wrote
last wins" paths.
Verified: all dd_state, dd_trajectory, per_sampler, final_reward,
plasticity oracle tests green; ml lib HOLDS the Phase 1.3.b-followup
baseline (947 pass / 12 fail) on RTX 3050 Ti — no new regressions.
Co-Authored-By: Claude Opus 4.7 (1M context) <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.