Detailed TDD implementation plan for wiring all 10 ML models into
the ensemble coordinator. Covers 5 new inference adapters (KAN, TGGN,
xLSTM, TLOB, Diffusion), main.rs registration with equal weights,
market data wiring, E2E integration test, Docker/CI validation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire existing components into end-to-end pipeline: 5 missing inference
adapters, 10-model registration, market data feeding, integration test,
Docker build validation, and CI pipeline triage.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
10-task plan: unify AssetClass, add trading_symbol field, futures_baseline()
preset, TOML config loading, DatasetSpec::from_universe() wiring, data
reorganization into cache structure, stale data cleanup, manifest generation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Define futures baseline universe (ES, NQ, ZN, 6E) with micro
contract mapping for limited capital. Reorganize scattered test
data into cache structure. Download spec for 730 days of OHLCV-1m.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
8-task plan across 3 phases: data pipeline (types, cache, manager,
prepared dataset), asset selection (universe, scorer, selector),
and integration wiring. 7 new files, ~51 tests estimated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Covers automated data downloading/caching for ML training and
a tiered asset selection funnel (universe → predictability → regime → signal).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three pillars: 7-gate conviction system, autonomous feedback loop
with kill switch, Rust-native model registry. QuestDB analytics layer.
~55 tests, 7 new files planned across ml/ and trading_service/.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
7-gate conviction system, autonomous feedback loop with kill switch,
and Rust-native model registry — backed by PostgreSQL + QuestDB.
Resolve merge conflicts in hyperopt/adapters/mod.rs and enhanced_ml.rs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add train_launcher.sh that detects local GPU VRAM and routes training
to local or Scaleway cloud. Auto-selects batch size per model based on
available VRAM tier. Maps model names to actual ml/examples/train_*.rs
cargo targets. Document Scaleway GPU instance types, setup procedure,
batch size tables, and cost estimates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
8 bite-sized tasks with TDD approach, exact file paths, and complete code.
Covers: DeviceConfig relocation, GpuCapabilities detection,
ModelMemoryEstimate per-model profiles, and wiring into PPO trainer
and hyperopt campaign.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Centralizes GPU device selection, capability detection, and per-model
batch size optimization to replace hardcoded RTX 3050 Ti limits.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Sequential build plan for 5 model integrations (TGGN, TLOB, KAN,
xLSTM, Diffusion) with TDD, worktree isolation, and full trait
implementations (UnifiedTrainable + ParameterSpace + hyperopt).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Approved design for expanding the ensemble with KAN, xLSTM, and
Diffusion architectures plus bringing TGGN/TLOB to full integration.
Sequential build order, no god classes, modular decomposition.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Phase 1: ML pipeline verification (checkpoint roundtrip, feature extraction)
Phase 2: Service production logic (hardcoded values, stub responses)
Phases 3-5: Backtesting, ML crate TODOs, infrastructure/security/cleanup
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Covers all TODO/FIXME items across workspace, checkpoint roundtrip
tests, feature extraction pipeline tests, and infrastructure cleanup.
No overlap with dedup-cleanup or liquid-cfc plans.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fixes discovered during live provisioning: app.ini permissions
(root:gitea + 660→640 for token generation), HTTPS via acme.sh
DNS-01, setcap for port 443, admin user creation, INSTALL_LOCK
lifecycle. Secrets replaced with placeholders.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
24 tasks across 5 pillars: runtime crash elimination, service wiring,
credential cleanup, clippy hardening, and remaining HIGH items.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bite-sized steps for each task with exact file paths, code snippets,
verification commands, and parallel execution strategy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Safety, live trading, security, and observability gaps identified
from comprehensive codebase audit. Organized for parallel swarm execution.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
8-task TDD plan to bridge 8 disconnected layers in the backtesting
pipeline: DBN converter, replay engine, 51-dim feature wiring,
model loader, registry startup, position tracking, PnL tracking,
and end-to-end integration test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
13-task plan covering data pipeline fixes (Databento stream, data acquisition,
DBN uploader) and trading service core wiring (RiskEngine, MLEngine, VaR,
feature extraction, order matching, API gateway, Prometheus panics).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
8-task plan to bridge disconnected backtesting pipeline layers:
DBN parser → feature extraction → ML inference → position tracking → PnL
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Covers data pipeline, trading service core, broker connectivity
(FIX 4.4 + TWS), execution algorithms, security/compliance, and
observability — based on comprehensive non-ML audit.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two-phase design:
A) Real 6E.FUT data → 4-model ensemble → PaperBroker → P&L metrics
B) Best hyperopt params → train → deploy to ensemble → ValidationHarness
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Create PpoStrategy and PpoLstmStrategy implementing ValidatableStrategy
to run PPO through walk-forward validation with DSR, PBO, and permutation
tests. Both variants validated on real 6E.FUT data (29,937 bars, 15 folds).
Key implementation detail: LSTM hidden states are detached from the
computation graph after each step to prevent stack overflow from
unbounded graph growth across 30k+ sequential forward passes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Single integration test that verifies the complete train → checkpoint →
validate pipeline works on real 6E.FUT data. 7 assertions covering
loss convergence, Q-value divergence, checkpoint round-trip, epsilon
decay, and Sharpe improvement vs untrained baseline.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Research-validated design for fixing critical DQN issues:
- CQL regularization for offline RL training on historical data
- IQN integration replacing broken C51 (Candle scatter_add bug)
- CVaR risk-aware action selection
- State dimension consolidation (51 is canonical)
- NaN panic fix, Adam epsilon fix per Rainbow paper
Verified against 20+ papers and 2026 SOTA.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove 1,307 AI-generated markdown files (984 in docs/, 323 in archive/).
No code references these files. Plans directory preserved.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
16-task bottom-up execution plan with exact file paths, commands,
and safety gates. Covers: doc purge, dead code removal, module
consolidation, test reorganization, file splitting, and folder
restructuring.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Comprehensive cleanup plan covering: documentation purge, dead code
removal (~4,846 lines across 11 files), module consolidation,
test reorganization, large file splitting, and folder restructuring.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add comprehensive logging utilities for DQN training with best practices:
- LoggingConfig: Configurable log levels, intervals, and sampling rates
- MetricsAggregator: Windowed statistics (mean, std_dev) for training metrics
- SampledLogger: Rate-limited logging for high-frequency events
Key features:
- Structured logging with tracing crate (info/debug/trace hierarchy)
- 23 unit tests for full coverage
- Integration with existing DQN training pipeline
Bug fixes:
- Fix u8 overflow in prioritized_replay.rs test (500 > u8::MAX)
- Fix GradStore assertion in residual.rs (no is_empty method)
- Fix Tensor::get() Option/Result handling in quantile_regression.rs
- Fix Device PartialEq comparison in ensemble_network.rs
Documentation:
- Add Rust logging best practices guide for ML training
- Add DQN logging analysis and design summary
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
