jgrusewski 4d4cd996db feat(sp21): T3.3 ISV defrost — hold_reward_ema (atomic Phase 3.2 wireup)
Closes the Phase 3.2 forward-reference loop in the SP20 aggregator.
Previously `out_inputs->per_bar_hold_reward = 0.0f` was hardcoded; the
per-bar Hold opportunity-cost producer existed
(experience_kernels.cu:3823 — `per_bar_opp_cost = -aux_conf × cost_scale`)
and wrote to `hold_baseline_buffer` and `r_micro` directly, but never
reached HOLD_REWARD_EMA. Result: HOLD_REWARD_EMA frozen at sentinel 0.0
across all observed epochs; the SP20 reward centering loop
(`r_micro += per_bar_opp_cost - HOLD_REWARD_EMA`) stayed uncentered,
biasing the policy's reward signal away from zero-mean.

T3.3 wireup mirrors the T2.2 alpha refactor: a per-env scratch buffer
that the producer writes at every step (alongside the existing
`hold_baseline_buffer` write), and the aggregator reads at the same
step. Sums opp_cost over Hold-direction envs only; emits the mean as
`per_bar_hold_reward`. The HOLD_REWARD_EMA's gate (`hold_fraction > 0.5f`
from T3.2) preserves the strict-majority semantic from before.

Atomic across producer site, kernel signature, aggregator, launcher,
collector, and tests (per feedback_no_partial_refactor):

  - experience_kernels.cu — new `float* per_bar_opp_cost_per_env`
    kernel arg, NULL-tolerant write at line ~3823.
  - sp20_aggregate_inputs_kernel.cu — new arg, 6th shmem stripe
    (`sh_opp_cost_sum`), per-thread Hold-gated accumulation, tree
    reduction extended to 6 stripes, output `per_bar_hold_reward =
    opp_cost_sum / hold_count` when hold_count > 0 else 0.
  - sp20_aggregate_inputs.rs — launcher signature, dynamic_shmem_bytes
    5→6 stripes, doc table, internal test.
  - gpu_experience_collector.rs — new `per_bar_opp_cost_per_env:
    CudaSlice<f32>` field, alloc, struct construction, kernel-arg
    pass at both experience_env_step and sp20_aggregate_inputs
    launches (raw_ptr).
  - sp20_aggregate_inputs_test.rs — helper renamed
    `run_kernel_with_is_win_and_opp_cost`, 4 existing sites pass
    NULL, 2 NEW oracle tests verifying real producer + NULL fallback.
  - sp20_phase1_4_wireup_test.rs — NULL fallback at the wireup site;
    HOLD_REWARD_EMA-stays-at-zero assertion remains valid.

Verification:
  - cargo check -p ml --tests: passes (warnings only)
  - cargo test -p ml --test sp20_aggregate_inputs_test --features cuda
    -- --ignored: 12/12 GPU oracle tests pass on RTX 3050 Ti, including
    both new T3.3 tests (per_bar_hold_reward_means_over_hold_envs_only,
    null_per_bar_opp_cost_emits_zero).
  - cargo test -p ml --test sp20_phase1_4_wireup_test --features cuda
    -- --ignored: 2/2 pass under the new NULL-tolerant contract.

Plan reference: docs/plans/2026-05-10-sp21-train-eval-coherence-isv-defrost.md
Tier 3 status: T3.1 ✓, T3.2 ✓, T3.3 ✓ (this commit), T3.4 withdrawn,
T3.5 cascade-pending, T3.6 withdrawn.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-10 20:35:39 +02:00

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.

Description
No description provided
Readme 849 MiB
Languages
Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
PLpgSQL 0.8%
Other 0.8%