b44a97ff94
Pure-instrumentation commit. Zero behavior change. The Gate CRT.1 smoke p9cnk (commit1e656948b) produced 222k trades and 29688% drawdown, worse than the failed CRT.A. The structural fixes (multi-horizon conviction, no-trade band, composite exit) all work as designed — but the trade cadence is fundamentally too fast for the signal/cost ratio. Before another round of threshold tuning, measure the structural truth. Adds device-side counters dumped at smoke close (single end-of-run memcpy_dtoh batch in read_diagnostics(), NOT per-event): Group A — per-horizon signal persistence: - flip_count[h]: total direction-sign flips - sum_run_length[h]: cumulative events between flips (u64) - run_length_hist[h]: 5-bucket log-scale histogram (1-9, 10-99, 100-999, 1k-9.9k, 10k+) Output: mean_run_len per horizon = signal half-life proxy Group B — conviction smoothed-EMA distribution: - conv_hist[10]: 10-bucket histogram over [0, 1) Output: shape of conviction distribution at decision time Group C — hold-time distribution: - hold_hist[6]: <1s, 1-10s, 10-60s, 1-10m, 10-60m, >1h Output: empirical distribution of trade hold times Group D — outcome by entry-conviction: - outcome_n[10]: trades per conviction bucket - outcome_sum_pnl[10]: cumulative realized PnL per bucket (price-units) - outcome_n_wins[10]: wins per bucket Output: validates "high conviction = better trades" hypothesis OR surfaces signal miscalibration Per-backtest memory: ~330 bytes. Negligible vs existing state. All counters single-writer-per-block (threadIdx.x == 0 convention; no atomicAdd per feedback_no_atomicadd). All updates kernel-internal (no host roundtrip per event). The end-of-run dump uses memcpy_dtoh in read_diagnostics() — called once at harness termination from the post-stop_ctrl_counters block, never per-event. Per feedback_no_htod_htoh_only_mapped_pinned, the hot path is untouched. N_HORIZONS = 5 (heads.rs canonical {30, 100, 300, 1000, 6000}); the plan text says 4 but the workspace constant is 5 — used the code value. Output at end of cluster smoke as a `crt_diag` log block: crt_diag h30: flips=X mean_run_len=Y events crt_diag h30 run_length_hist: 1-9:N 10-99:N 100-999:N ... ... crt_diag conv_ema_hist: [0.0-0.1]:N [0.1-0.2]:N ... crt_diag hold_time_hist: <1s:N 1-10s:N ... crt_diag outcome_by_entry_conv[0.0-0.1]: n=N win_rate=X.X% mean_pnl_pu=Y.YYY ... The output drives the next round of CRT.1 threshold design (delta_floor, composite exit factors, min-hold cooldown) from data instead of guesses. Per pearl_adaptive_not_tuned and pearl_controller_anchors_isv_driven: thresholds will become ISV-derived in CRT.2; CRT.1 must first establish defensible defaults from the empirical signal characteristics. Verified: stop_controller (22/22 pass), decision_floor_coldstart (3/3 pass), threshold_and_cost (3/3 pass), parallel_sim_correctness (1/1 pass), lob_sim_integrated_fuzz (3/3 pass). Two lob_sim_fixtures failures (fix_decision_alpha_buy_close, fix_decision_program_h4_only) were pre-existing at HEAD1e656948b— not caused by this commit. Co-Authored-By: Claude Opus 4.7 (1M context) <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.