a5de50503f
Phase E Task 4. Medium-tier fill simulator per the design memo.
For each (level ∈ {L1,L2,L3}, side ∈ {Bid,Ask}):
λ(features) = exp(β · [1, spread_bps, L1_imb, OFI_5, log(τ+1)])
Per-snapshot Bernoulli fill probability for a posted limit order:
p = 1 − exp(−λ)
Market orders fill immediately at the opposite-side L1 quote (no slippage
modeled at medium tier). Closing actions (Side::None) return λ=0 — they
are handled separately in the env.
Scaffolding only. Task 5 fits the 30 coefficients (6 distributions × 5
features) from the 5.2M-trade historical tape. The skeleton constructor
uses β=0 → λ=1 → p≈0.632, a stable sanity default for early smokes.
Numeric guard: `linear.exp().min(50.0)` caps λ to prevent f32 overflow
under outlier features before fitting lands. Fitted models should stay
well below this cap in practice.
4 unit tests:
- skeleton_has_uniform_fill_prob (β=0 → p≈0.632 within 0.01)
- fill_prob_bounded_under_outlier_features
- lambda_cap_prevents_f32_overflow (β=100 outlier path)
- side_none_returns_zero_rate
`cargo test -p ml --lib env::fill_model`: 4 passed.
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;