jgrusewski 7064c9269e fix(rl): un-freeze adaptive WIN/LOSS clamp + reset per-batch state on fold
Two compounding bugs found via cluster diag at alpha-rl-gwmkf step 4083:

Fix C — reward-clamp writeback un-freeze:
  rewards.clip_rate_ema = 0.9999 — essentially every win clamped to +1.
  With R-multiple 43 trade outcomes ($7k avg win / $163 avg loss), V
  regression saw identical targets for +$500 wins and +$42k wins; PPO
  advantage lost magnitude signal; policy stagnated at qpa = -0.95.

  Root cause: a 2026-05-24 "G.2" patch in rl_reward_clamp_controller.cu
  added `(void) margin;` and froze WIN=1.0, LOSS=3.0 over concern that
  simultaneously adapting clamp bounds + atom span could create a
  positive feedback loop. Empirically refuted — MARGIN is bounded
  [1.0, 5.0] and pos_max_ema is bounded by reward_scale × raw_PnL, so
  WIN has natural structural ceiling. The freeze was over-conservative.

  Restore the writeback: WIN = max(MIN_WIN, MARGIN × pos_max_ema),
  LOSS = max(MIN_WIN, WIN × RATIO). Atom span Step 5 then follows via
  the slow EWMA already in place.

  Local b=128 1k smoke confirms:
    clip_rate_ema: 0.9999 → 0.094  (target 5%, near hit)
    WIN clamp:     1.0    → 245.6
    V_max atom:    1.0    → 1929
    qpa:           -0.95  → +0.71  (Q and π aligned!)
    mean_active:   +$47k stays positive

Fix D — reset_session_state extends to per-batch buffers:
  The 39efacf77 per-batch CMDP refactor added 4 device buffers but
  reset_session_state() was only zeroing the 4 ISV summary slots. On
  train→eval fold transition, eval inherited all the accumulated
  dead/cooldown accounts from training — eval started with a poisoned
  fleet.

  reset_session_state now also memsets session_pnl_per_batch_d,
  session_dd_triggered_per_batch_d, consec_loss_per_batch_d, and
  cooldown_remaining_per_batch_d to zero. Plus also resets the new
  RL_SESSION_PNL_WORST_INDEX slot.

Validation: 13/13 risk_stack_invariants pass, 20/20 trade_management
pass, integrated_trainer_smoke end-to-end passes.
2026-05-30 23:19:20 +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
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