bc9eaac89d
B-5 (asymmetric α with static α_slow=0.001) revealed the static parameter problem: provably bounds cascades (avg_win peak $2k vs B-4's $40k) BUT over-conservative in train (dckcc step 800: avg_l > avg_w → Kelly says don't trade → model can't discover edges; wr_ema crashed to 0.145). The fundamental tension: static α_slow can't satisfy BOTH - Train convergence: asymmetry must FADE so model learns from real data - Boundary safety: asymmetry must ENGAGE at every fold to prevent cascade B-6 RESOLVES this via Bayesian shrinkage: trust(n) = min(1, cum_dones / n_full_threshold) [Phase 1] stability = exp(-CV × cv_gain) [Phase 2] trust_eff = trust(n) × stability α_slow_eff = α_slow_min + (α_fast − α_slow_min) × trust_eff Phase 1 (data-quantity): trust grows with cum_dones; reset_session_state zeroes cum_dones → asymmetry RESUMES at every boundary. Math: at n=0 α_slow_eff = α_slow_min = 0.001 (full skepticism). At n=n_full = 30k trades: α_slow_eff = α_fast = 0.05 (full standard Wiener). Phase 2 (data-quality): Welford CV of reward magnitude gates trust. Stable signal (CV→0): stability=1, trust opens normally. Volatile signal (CV high): stability→0, asymmetry persists. cv_gain=0 disables Phase 2. Per-EMA asymmetry direction encodes Kelly safety semantics: avg_win: slow-up (skeptical of wins), fast-down avg_loss: fast-up (admit losses), slow-down (slow forget) wr_ema: slow-up (skeptical of high WR), fast-down ISV slots (all signal-derived from cum_dones + Welford): 721 RL_EMA_ALPHA_SLOW_MIN_INDEX = 0.001 722 RL_EMA_TRUST_FULL_THRESHOLD_INDEX = 30000 723 RL_EMA_CV_GAIN_INDEX = 1.0 Threshold calibration (n_full=30k): - Train: ~1000 cluster steps for trust to fully open → asymmetry active during cold-start (first 30 steps, cascade prevention) then fades. - Eval: 30 dones/step × 500 eval steps = 15k dones → trust climbs to 0.5 by eval end → partial protection throughout eval. Composes: - B-3 Kelly fractional-trust (Kelly SIZING gated by cum_dones) - B-6 EMA asymmetric-α (Kelly INPUTS biased conservative by cum_dones) Both fade as data accumulates; both reset at boundary. Spec: docs/superpowers/specs/2026-06-01-ema-asymmetric-trust-with-cv-gain.md 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.