87a22d12c9
Adds the minimum-viable implementation of the R9 multi-fold G8 gate
per `pearl_single_window_oos_is_not_oos` ("a single window is NOT
out-of-sample"). The trainer can now:
1. Slice the MBP-10 file list into K equal-sized blocks
(`--n-folds K --fold-idx k`).
2. Train on blocks [0..=k] (passed to MultiHorizonLoader).
3. Run a separate eval phase of `--n-eval-steps` on block [k+1]
using a second loader instance.
4. Drain LobSim trade records gated by a pre-eval head checkpoint
so train-phase trades don't contaminate the eval summary.
5. Compute profit_factor + sharpe + drawdown via existing
`ml_backtesting::artifacts::compute_summary`.
6. Write `eval_summary.json` alongside `alpha_rl_train_summary.json`.
## Manual fan-out (this MVP)
The dispatcher (`scripts/argo-alpha-rl.sh`) gains three new flags
that thread through the Argo template into the CLI: `--fold-idx`,
`--n-folds`, `--n-eval-steps`. To run a 3-fold G8:
./scripts/argo-alpha-rl.sh --n-folds 3 --fold-idx 0 --n-eval-steps 200
./scripts/argo-alpha-rl.sh --n-folds 3 --fold-idx 1 --n-eval-steps 200
(With n_folds=3 the valid fold indices are 0 and 1 — the third block
is the eval window for fold 1. n_folds=K accepts fold_idx ∈ [0, K-2].)
Each submission produces one `eval_summary.json` at the resolved
output dir; the per-fold profit_factor is the value to aggregate.
Manual aggregation for now — automated DAG matrix fan-out + an
in-cluster aggregator pod is a follow-up commit. The aggregator
will mean ± SD the per-fold PFs and gate on `PF > 1.0`.
## What's NOT pure eval
The eval loop calls `step_with_lobsim` (same as train) — Adam steps,
PER updates, controller adaptations all still fire during eval. At
b_size=1 the per-step learning effect is small relative to the
train-phase-accumulated policy, so the eval PF approximates the
OOS performance of the train-end policy. A clean pure-eval mode
(forward + LobSim step only, no backward/Adam/PER) is a follow-up
architectural change; documented inline at the eval phase block.
## Default behaviour unchanged
`--n-folds=1` (default) skips the eval split entirely and uses all
files for training — identical to the prior single-window smoke.
The R9 prior smokes ran in this mode. Default `--fold-idx=0` and
`--n-eval-steps=0` keep prior smoke runs binary-compatible.
## Template + dispatcher changes
* `alpha-rl-template.yaml`: adds 3 new workflow parameters
(`fold-idx`, `n-folds`, `n-eval-steps`) and threads them into
the train container's `alpha_rl_train` invocation.
* `argo-alpha-rl.sh`: adds matching CLI flags with explicit
documentation of the multi-fold dispatching pattern.
## Verified gates
Local sm_86 build + dispatcher syntax clean. Tests unchanged
(the walk-forward path is exercised by cluster smokes, not unit
tests — the loader-slicing logic is straightforward index math).
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.