d1638959d3
Single-file fix to `compute_epoch_financials`: remove the `if n_returns_f >= bars_per_year` short-rollout fallback that left v2 semantics in place for sub-year rollouts. For Foxhunt's volume bars, `bars_per_day ≈ 34_496` → `bars_per_year ≈ 8.69M`, while a training epoch produces `n_returns ≈ 4.10M`. The guard fired on every production epoch, so the v3 CAGR fix was a no-op. Diagnosis chain: - v3 commit (2937da889) merged the n_returns >= bars_per_year guard - Smoke v4 (commit62b5a50e8, workflow train-frv8x) epoch 1 showed Return=+2.963e2% — bit-identical to v1's pre-fix output - Hypothesis 1 (cache poisoning): ruled out — ensure-binary log shows "Cache MISS: compiling binaries for 62b5a50e8" and ml crate was recompiled fresh - Hypothesis 2 (different commit): ruled out — workflow params confirm commit-sha =62b5a50e8= current HEAD - Hypothesis 3 (bars_per_year mismatch): confirmed — v4 log emits "Bars per day (from data): 34496" which makes bars_per_year > n_returns and triggers the v2 fallback inside the v3 branch Fix: unconditional CAGR. The log-space clamp [-23, +20] bounds the display in all edge cases (tests with tiny n_returns extrapolate aggressively; the clamp caps at exp(20) - 1 ≈ +4.85e8%). Expected v5 epoch 1 Return: ~+1.770e3% (was +2.963e2% under v4). The new value is the *actual annualized* projection: 1377% over a 0.47-year rollout. Overfit cycles cap at +4.85e8% (was +e19%). Tests: - cargo test -p ml --lib financials → 7/7 - Sign-only assertions in test cases (all > 0.0) — no regressions Files changed: - crates/ml/src/trainers/dqn/financials.rs: 1 conditional removed, comment block updated with v3 → v3.1 history - docs/dqn-wire-up-audit.md: diagnosis + fix entry for 2026-05-11 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;