12fdd18223
Deleted: - DQN::compute_loss_internal (280 lines) — old Candle forward+loss - DQN::train_step (55 lines) — old Candle training step - DQN::compute_gradients (47 lines) — old gradient accumulation - ComputeLossResult struct — only used by deleted functions - RegimeConditionalDQN::train_step (65 lines) — old dispatch - RegimeConditionalDQN::train_step_gpu_regime (100 lines) — old GPU path - RegimeConditionalDQN::compute_gradients_gpu (130 lines) — old regime gradients - RegimeConditionalDQN::compute_gradients (92 lines) — old dispatch - DQNAgentType::train_step dispatch — dead - DQNAgentType::compute_gradients dispatch — dead - GpuDqnTrainer::upload_batch (71 lines) — old CPU→GPU upload - train_step.rs (500 lines) — entire module including ensure_fused_ctx - dqn_benchmark.rs — used old train_step - examples.rs — used old train_step - validation/adapters.rs (289 lines) — used old train_step - dqn/trainable_adapter.rs — used old train_step - gpu_smoketest.rs — tested old train_step - Gradient accumulation path in training_loop.rs (144 lines) - IQN d_h_s2().clone() → raw pointer (zero alloc) - Causal intervention format! string alloc removed - Dead HER relabel functions (320 lines) Kept: - ensure_fused_ctx logic inlined into training_loop.rs - set_noise_sigma_scale re-added to RegimeConditionalDQN Fixed: - GpuReplayBuffer max_batch_size wired from batch_size parameter (was hardcoded 1024, blocking batch_size=8192) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
4.7 KiB
4.7 KiB
Foxhunt — HFT Trading System
Behavioral Rules
- Do what has been asked; nothing more, nothing less
- NEVER create files unless absolutely necessary
- ALWAYS prefer editing an existing file to creating a new one
- NEVER proactively create documentation files (*.md) unless explicitly requested
- NEVER save working files, text/mds, or tests to the root folder
- ALWAYS read a file before editing it
- NEVER commit secrets, credentials, or .env files
Project Overview
Rust-based high-frequency trading system with ML-driven strategies (DQN, PPO, TFT, Mamba2), deployed on Kubernetes (Scaleway Kapsule) via Argo Workflows.
Tech Stack
| Component | Technology |
|---|---|
| Language | Rust 1.85+, Edition 2021 |
| Async | Tokio 1.40 (multi-threaded) |
| RPC | Tonic 0.14 (gRPC), tonic-web (browser gateway) |
| Database | PostgreSQL 16 (SQLx, offline mode), Redis 7, QuestDB 8.2.3 |
| ML | DQN, PPO, TFT, Mamba2 (Rust inference) |
| Frontend | React 19, TypeScript, Tailwind CSS, Vite (web-dashboard/) |
| Orchestration | Kubernetes + Argo Workflows |
| Build Cache | sccache (MinIO S3 backend) |
| GPU | CUDA 12.4, L40S (48GB), H100 (80GB) |
Project Structure
| Directory | Purpose |
|---|---|
crates/ (40 crates) |
Core libs: trading engine, ML models, data, backtesting, risk mgmt |
services/ (8 services) |
Microservices: API, Trading, ML Training, Data Acquisition, Backtesting, Broker Gateway, Trading Agent |
bin/fxt/ |
CLI binary |
web-dashboard/ |
React dashboard (gRPC-web) |
testing/ |
Integration, e2e, load, stress tests |
infra/k8s/ |
K8s manifests, Argo Workflows, network policies, monitoring |
infra/docker/ |
Dockerfiles (ci-builder, runtime, training-runtime) |
scripts/ |
Build, deployment, health-check, GPU scripts |
migrations/ |
PostgreSQL migrations |
proto/ |
Protocol Buffer definitions |
config/ |
Configuration files and templates |
Build & Test
# Build
cargo build --release
cargo build --profile=dev-release # Fast iteration (thin LTO)
cargo build -p <service-name> --release # Single service
# Test
cargo test --workspace
cargo test -p <crate-name>
cargo test --test integration
# Benchmarks
cargo bench --bench performance_regression
cargo bench --bench trading_latency
cargo bench --bench database_performance
SQLX_OFFLINE=true— compiled SQL queries (no live DB needed for build)SCCACHE_BUCKET=foxhunt-sccache— shared compile cache
Deployment (Argo Workflows)
./scripts/argo-compile-deploy.sh [--branch main] [--commit <sha>]
./scripts/argo-train.sh [--model dqn|ppo|supervised]
- Node pools:
ci-compile-cpu(compile/test),ci-training(L40S GPU),ci-training-h100(H100 GPU) - Training data: PVC at
/mnt/training-data/futures-baseline - Docker: Kaniko push to Scaleway Container Registry
- Legacy CI:
.gitlab-ci.ymlexists but disabled (when: never), Argo is primary
File Organization Rules
- NEVER save to root folder
/src,/crates— Rust source code/tests,/testing— test files/docs— documentation/config— configuration/scripts— utility scripts/infra— infrastructure (K8s, Docker)
Security Rules
- NEVER hardcode API keys, secrets, or credentials
- NEVER commit .env files or trading credentials
- Always validate input at system boundaries
- Sanitize file paths to prevent directory traversal
- Broker credentials (AMP Futures/CQG FIX 4.4) are K8s secrets — never log them
Concurrency: 1 MESSAGE = ALL RELATED OPERATIONS
- All operations MUST be concurrent/parallel in a single message
- Use Claude Code's Task tool for spawning agents
- ALWAYS batch ALL todos in ONE TodoWrite call
- ALWAYS spawn ALL agents in ONE message via Task tool
- ALWAYS batch ALL file reads/writes/edits in ONE message
- ALWAYS batch ALL Bash commands in ONE message
RuFlo V3 Integration
Hooks are configured in .claude/settings.json and run local scripts from .claude/helpers/. All hooks have timeouts — no network calls on the hot path.
CLI Commands
npx ruflo@latest status # Check status
npx ruflo@latest hooks metrics # View learning metrics
npx ruflo@latest hooks pretrain # Bootstrap from repository
npx ruflo@latest hooks build-agents # Generate agent configs
important-instruction-reminders
Do what has been asked; nothing more, nothing less. NEVER create files unless they're absolutely necessary for achieving your goal. ALWAYS prefer editing an existing file to creating a new one. NEVER proactively create documentation files (*.md) or README files. Only create documentation files if explicitly requested by the User. Never save working files, text/mds and tests to the root folder.