cb2015ab2f
Restores regime adaptation to the 0.5% trailing stop using ADX (trend
strength, feat idx 40) and CUSUM (directional persistence, feat idx 41).
Previously removed to eliminate train/val asymmetry (val kernel had no
features buffer); this commit wires features to both kernels so the
stop fires on identical thresholds in training and backtest evaluation.
Implementation (V7 methodology, all three steps satisfied):
* Step 1 signal: trending regimes need wider stop to ride trend;
volatile regimes need wider stop to avoid noise exits. Original
formulation from reward v5 (commit 263997ad3).
* Step 2 coverage: no existing mechanism adapts exit *distance* to
regime — Kelly cap scales entry *size*, policy chooses exit *timing*
but not exit *distance*. So this is a genuine gap.
* Step 3 measurement: shared compute_regime_trail_scales helper
guarantees bit-identical scale computation across training and val;
multi-trial smoke (5 trials × 20 epochs) passes 5/5 with
median_q_gap=2.00, Best Sharpe 27.05 in last trial. No regression
vs fixed-width (pre: median_q_gap=2.24; post: 2.00, within variance).
Shared helper (trade_physics.cuh::compute_regime_trail_scales):
trend_scale = min(2.5, 1 + max(ADX - 0.25, 0) * 2.0)
vol_scale = min(2.5, 1 + max(|CUS| - 0.50, 0) * 2.0)
Pass NULL features to fall back to 1.0/1.0 (fixed-width). Thresholds
match existing regime_conditional convention (adx>0.25=Trending).
Kernel-side:
* unified_env_step_core: add vol_scale / trend_scale params; forward
to check_trailing_stop (which already accepted these but callers
passed 1.0/1.0). Updated step-6 docstring.
* experience_env_step (training): computes scales from existing
features+market_dim inputs before calling the helper.
* backtest_env_step + backtest_env_step_batch (val): new `features` +
`market_dim` params plumbed through the single-step and batched
launches.
Rust-side (gpu_backtest_evaluator.rs):
* launch_env_step: pass self.features_buf + feature_dim as i32
(features were already uploaded for the state_gather kernel).
* Batched launcher: same.
Net effect: train/val env kernels now see identical regime-adaptive
trailing stops — no drift, no measurement gap, symmetric physics.
Verified:
cargo check/build clean (release); multi-trial smoke 5/5 pass.
Closes task #25.
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;