fa8d546614
Per feedback_adaptive_not_tuned.md: adaptive signal-driven mechanism, zero
static tuning knobs. Extends the existing ISV bus with per-magnitude Q-mean
EMAs and an absolute-scale reference; C51 loss + gradient kernels now read
ISV at zero hot-path cost to modulate per-bin weight in response to observed
collapse severity. Weight is 1.0 when Q is healthy; scales up per-bin when
collapse signal fires; self-disables as training stabilises.
Additions:
* ISV_DIM 13 → 17. New slots:
[13] Q_MAG_MEAN_QUARTER: ema(mean Q(Quarter), tau=0.05)
[14] Q_MAG_MEAN_HALF: ema(mean Q(Half), tau=0.05)
[15] Q_MAG_MEAN_FULL: ema(mean Q(Full), tau=0.05)
[16] Q_ABS_REF: ema(max(|Q_mean[k]|), tau=0.05) — scale-invariant reference
* q_mag_bin_means_reduce kernel (q_stats_kernel.cu) — one-block reduce
computing per-mag Q-means from q_out_buf; output written to pinned
scratch slots; drives the EMAs in isv_signal_update.
* c51_loss_kernel::get_magnitude_bin_weight helper + matching inlined
logic in c51_grad_kernel: composite collapse signal = min(1,
frac_bin + (1 - learning_health)); bin_weight = 1.0 + collapse *
mag_bias_signal[k] (bounded in [1, 2]); mag_bias_signal[k] = (k+1)/b1_size.
* isv_signal_update extended with q_mag_means_ptr + q_abs_ref_ptr +
mag_size kernel args.
Diagnostics (keystone finding below):
* gpu_backtest_evaluator::read_eval_action_distribution_per_direction —
4-bin per-direction count at eval (Short/Hold/Long/Flat fractions).
This diagnostic flipped the task diagnosis.
* DQNTrainer::last_eval_direction_dist accessor.
* last_isv_magnitude_bin_q_means accessor.
* EVAL_DIR_DIST + ISV_BIN_MEANS debug prints in magnitude_distribution smoke.
* ef >= 0.05 smoke gate added (currently unreachable behind pre-existing
eh+ef >= 0.30 gate; kept for future use).
Training-time outcome:
Pre-fix MAG_DIST: Quarter=0.60 Half=0.10 Full=0.23
Post-fix MAG_DIST: Quarter=0.46 Half=0.24 Full=0.28 (2.4× Half lift,
Full unchanged)
Pre-fix EVAL_DIST: eq=1.000 eh=0.000 ef=0.000
Post-fix EVAL_DIST: eq=0.981 eh=0.019 ef=0.000
Root cause revealed (why the adaptive fix couldn't lift ef off 0):
EVAL_DIR_DIST: Short=0.045 Hold=0.115 Long=0.070 Flat=0.771
~88% of eval states have direction ∈ {Hold, Flat}. Kernel at
experience_kernels.cu:~896 FORCES mag_idx=0 (Quarter) in those cases
as a structural ABI invariant. Only ~11.5% of eval samples have a
free magnitude choice. Upper bound on ef regardless of magnitude
mechanism: ~0.11.
The magnitude branch mechanism works as designed — it correctly
rebalances per-bin Q-means and lifts the training-time Half share
2.4×. But direction-branch collapse to Flat masks everything
downstream. Task 2.X's magnitude-only scope cannot unblock eval ef.
The real fix target is direction-branch eval collapse. Follow-up
task "Task 2.Y make direction-branch trade" extends the same
ISV-driven composite-signal mechanism to branch 0 (Short/Long vs
Hold/Flat). Scoping doc to be written.
Smoke validation:
magnitude_distribution FAIL (pre-existing eh+ef >= 0.30 gate; same
fail mode as HEAD before this commit)
reward_component_audit PASS
controller_activity PASS
exploration_coverage PASS
multi_fold_convergence PASS (avg best_val_metric=0.039, within ±15%)
No config fields. No static tuning knobs. No feature flags. All
modulation flows through the ISV bus. Shape constants documented:
eps=1e-6 (numerical guard), alpha=0.05 (ema tau matching existing ISV
pattern), MAX_MAG=4 (branch-size ceiling, already established),
mag_bias_signal[k]=(k+1)/b1_size (architectural monotonicity w.r.t.
bin index as stake size).
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.