jgrusewski 0c9d1ee39e fix(dqn): Kelly cap warm-branch deadlock — effective_kelly never collapses to zero
VAL DIAGNOSTIC PROOF (train-4fpzx step=400):
  pick=Long Full (target=0.66) prev_pos=0.0 → pos_post=0.0 actual_dir=Flat
  trail=0, margin can't clip to 0 → only Kelly cap zeroed the target.

ROOT CAUSE:
  effective_kelly = maturity * kelly_f + (1 - maturity) * warmup_floor

  Cold-start fix (commit 2c97e0436) protected `warmup_floor` so it never
  collapses to zero at maturity=0. But the warm branch was left exposed:
  once maturity → 1 (>=10 completed trades), the blend collapses to
  `kelly_f` alone, and `kelly_f = 0` is the natural state of
    (payoff*win_rate - (1-win_rate)) / payoff
  with balanced priors and small actual returns. Val environments with
  pure per-bar P&L (no saboteur/shaping perturbations like training)
  settle into this regime within ~10 trades — after which every non-Hold
  target gets clamped to 0 deterministically. Identical bootstrap-deadlock
  pattern to the IQN trunk SAXPY.

EVIDENCE:
  val_picked_dir_dist [short=0.19 hold=0.20 long=0.39 flat=0.21]  (kernel pick)
  val_dir_dist        [short=0.0001 hold=0.20 long=0.0000 flat=0.80]  (post-physics)
  100% of Long picks and ~99.95% of Short picks become actual_dir=Flat.
  Hold passes through 1:1 (Hold skips margin/Kelly/trail in env_step).

  VALDIAG step=400: act=77 (Long Full, target=0.66) prev=0.0 pos_post=0.0
  -> confirms target zeroed before execute_trade; trail=0 rules out trail;
  margin cap can't produce 0 with equity=$35K vs margin/contract=$17.9K.

FIX:
  effective_kelly = max(kelly_f, warmup_floor)

  The conviction-and-health-driven warmup_floor (in [0.5, 1.0]) becomes a
  permanent minimum cap. `kelly_f` only takes over when the policy has
  demonstrated enough edge to *exceed* the floor. Preserves design intent
  (Kelly drives sizing once stats mature with real edge) while preventing
  the bootstrap deadlock in environments where balanced trades naturally
  yield kelly_f = 0.

  All adaptive ISV-driven signals; no tuned constants.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-26 21:41:33 +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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Cuda 7.7%
Python 1.3%
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