c3b5e124f0
Ignore ML checkpoints, trained model safetensors, stray ml/ml/ dir, and .claude/worktrees/. Clean up duplicate hive-mind-prompt entries. Add 17 design/implementation plan docs from 2026-02-20 to 2026-02-22. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1017 lines
31 KiB
Markdown
1017 lines
31 KiB
Markdown
# Full-Stack ML Integration Implementation Plan
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> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
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**Goal:** Take the ML codebase from "4 models that train independently" to "paper-trading pipeline producing simulated P&L from a 4-model ensemble."
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**Architecture:** 4-phase approach: (1) delete 88 stale test files and commit unstaged adapter code, (2) wire all 4 model adapters into the EnsembleCoordinator, (3) build hyperopt campaign runner with automated validation, (4) add paper-trading pipeline with simulated fills and P&L tracking.
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**Tech Stack:** Rust, candle (ML framework), safetensors (checkpoints), tokio (async), Databento (market data)
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---
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## Phase 1: Codebase Cleanup
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### Task 1: Delete stale test files using `WorkingDQN`
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88 test files in `ml/tests/` reference the removed `WorkingDQN`/`WorkingDQNConfig` types and will not compile. They must be deleted.
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**Files:**
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- Delete: All 88 files listed below
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**Step 1: Delete all stale WorkingDQN test files**
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Run:
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```bash
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cd /home/jgrusewski/Work/foxhunt
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grep -rl "WorkingDQN\|WorkingDQNConfig" ml/tests/ | xargs rm -f
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```
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**Step 2: Verify deletion**
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Run: `ls ml/tests/*.rs | wc -l`
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Expected: ~482 files remain (570 - 88 = ~482)
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**Step 3: Commit**
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```bash
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git add -u ml/tests/
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git commit -m "chore(ml): delete 88 stale test files referencing removed WorkingDQN type"
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```
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---
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### Task 2: Fix test files using wrong crate name `foxhunt_ml::`
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4 test files use `foxhunt_ml::` instead of the correct `ml::` crate name.
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**Files:**
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- Fix: `ml/tests/dqn_full_gradient_flow_integration_test.rs`
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- Fix: `ml/tests/dqn_gradient_flow_isolation_test.rs`
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- Fix: `ml/tests/tft_int8_forward_integration_test.rs`
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- Fix: `ml/tests/tft_int8_integration_test.rs`
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**Step 1: Fix crate name in all 4 files**
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Run:
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```bash
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cd /home/jgrusewski/Work/foxhunt
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sed -i 's/foxhunt_ml::/ml::/g' \
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ml/tests/dqn_full_gradient_flow_integration_test.rs \
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ml/tests/dqn_gradient_flow_isolation_test.rs \
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ml/tests/tft_int8_forward_integration_test.rs \
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ml/tests/tft_int8_integration_test.rs
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```
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**Step 2: Check if the fixed files compile**
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Run: `SQLX_OFFLINE=true cargo check -p ml --tests 2>&1 | grep -i "error\[" | head -20`
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If these files have other compilation errors beyond the crate name, delete them too:
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```bash
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rm -f ml/tests/dqn_full_gradient_flow_integration_test.rs \
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ml/tests/dqn_gradient_flow_isolation_test.rs \
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ml/tests/tft_int8_forward_integration_test.rs \
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ml/tests/tft_int8_integration_test.rs
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```
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**Step 3: Commit**
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```bash
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git add ml/tests/
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git commit -m "fix(ml): fix or remove test files with wrong foxhunt_ml:: crate name"
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```
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---
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### Task 3: Commit unstaged ensemble adapter files
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The Mamba2 and TFT inference adapters already exist but are unstaged. The `adapters/mod.rs` already exports them.
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**Files:**
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- Stage: `ml/src/ensemble/adapters/mod.rs` (modified — added mamba2/tft modules)
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- Stage: `ml/src/ensemble/adapters/mamba2.rs` (new — 320 lines, with tests)
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- Stage: `ml/src/ensemble/adapters/tft.rs` (new — 484 lines, with tests)
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**Step 1: Run the adapter unit tests**
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Run:
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```bash
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SQLX_OFFLINE=true cargo test -p ml --lib ensemble::adapters::mamba2 -- --nocapture
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SQLX_OFFLINE=true cargo test -p ml --lib ensemble::adapters::tft -- --nocapture
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```
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Expected: All tests pass (3 tests each module)
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**Step 2: Commit**
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```bash
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git add ml/src/ensemble/adapters/mod.rs ml/src/ensemble/adapters/mamba2.rs ml/src/ensemble/adapters/tft.rs
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git commit -m "feat(ensemble): add Mamba2 and TFT inference adapters with ring buffer + tests"
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```
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---
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### Task 4: Commit unstaged Mamba2 loss module
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The directional MSE loss function and bilinear discretization fix are unstaged.
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**Files:**
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- Stage: `ml/src/mamba/loss.rs` (new — 112 lines)
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- Stage: `ml/src/mamba/mod.rs` (modified — bilinear discretization fix)
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**Step 1: Verify Mamba2 tests still pass**
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Run: `SQLX_OFFLINE=true cargo test -p ml --lib mamba -- --nocapture 2>&1 | tail -10`
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Expected: Tests pass
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**Step 2: Commit**
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```bash
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git add ml/src/mamba/loss.rs ml/src/mamba/mod.rs
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git commit -m "feat(mamba): add directional MSE loss and bilinear discretization fix"
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```
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---
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### Task 5: Verify full compilation
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**Step 1: Check lib compilation**
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Run: `SQLX_OFFLINE=true cargo check -p ml --lib 2>&1 | tail -5`
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Expected: No errors
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**Step 2: Check remaining integration tests compile**
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Run: `SQLX_OFFLINE=true cargo check -p ml --tests 2>&1 | grep "error\[" | wc -l`
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Expected: 0 errors (or identify remaining broken files and delete them)
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**Step 3: If errors remain, delete offending files and re-commit**
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```bash
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# Identify remaining broken test files
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SQLX_OFFLINE=true cargo check -p ml --tests 2>&1 | grep "error\[" | grep -oP 'ml/tests/\S+\.rs' | sort -u | xargs rm -f
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git add -u ml/tests/
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git commit -m "chore(ml): remove remaining broken integration test files"
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```
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**Step 4: Run lib tests**
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Run: `SQLX_OFFLINE=true cargo test -p ml --lib 2>&1 | tail -20`
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Expected: All pass, zero failures
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---
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## Phase 2: Ensemble Coordinator Integration
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### Task 6: Create `InferenceEnsemble` — lightweight coordinator wrapping 4 adapters
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The existing `EnsembleCoordinator` in `coordinator.rs` is async and uses `Features`/`ModelPrediction` types from the old ensemble system. Rather than refactoring it, create a new lightweight synchronous coordinator that uses the `ModelInferenceAdapter` trait.
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**Files:**
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- Create: `ml/src/ensemble/inference_ensemble.rs`
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- Modify: `ml/src/ensemble/mod.rs` (add `pub mod inference_ensemble;`)
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**Step 1: Write the failing test**
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In `ml/src/ensemble/inference_ensemble.rs`:
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```rust
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::ensemble::inference_adapter::{
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EnsemblePrediction, FeatureVector, ModelInferenceAdapter, PredictionMeta,
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};
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/// Dummy adapter for testing ensemble aggregation
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struct DummyAdapter {
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name: String,
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direction: f64,
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confidence: f64,
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ready: bool,
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}
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impl ModelInferenceAdapter for DummyAdapter {
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fn model_name(&self) -> &str { &self.name }
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fn is_ready(&self) -> bool { self.ready }
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fn predict(&self, _features: &FeatureVector) -> crate::MLResult<EnsemblePrediction> {
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Ok(EnsemblePrediction {
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model_name: self.name.clone(),
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direction: self.direction,
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confidence: self.confidence,
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metadata: PredictionMeta {
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latency_us: 100,
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quantiles: None,
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attention_weights: None,
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q_values: None,
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},
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})
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}
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}
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#[test]
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fn test_ensemble_equal_weight_aggregation() {
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let adapters: Vec<Box<dyn ModelInferenceAdapter>> = vec![
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Box::new(DummyAdapter { name: "A".into(), direction: 1.0, confidence: 0.8, ready: true }),
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Box::new(DummyAdapter { name: "B".into(), direction: -1.0, confidence: 0.6, ready: true }),
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];
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let ensemble = InferenceEnsemble::new(adapters);
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let fv = FeatureVector { values: vec![0.0; 51], timestamp: 0 };
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let result = ensemble.predict(&fv).unwrap();
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// Weighted: (1.0*0.8 + (-1.0)*0.6) / (0.8+0.6) = 0.2/1.4 ≈ 0.143
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assert!(result.direction > 0.0, "Net direction should be positive");
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assert!(result.direction < 0.5, "Net direction should be modest");
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}
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#[test]
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fn test_ensemble_skips_unready_models() {
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let adapters: Vec<Box<dyn ModelInferenceAdapter>> = vec![
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Box::new(DummyAdapter { name: "A".into(), direction: 1.0, confidence: 0.9, ready: true }),
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Box::new(DummyAdapter { name: "B".into(), direction: -1.0, confidence: 0.9, ready: false }),
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];
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let ensemble = InferenceEnsemble::new(adapters);
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let fv = FeatureVector { values: vec![0.0; 51], timestamp: 0 };
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let result = ensemble.predict(&fv).unwrap();
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// Only model A is ready, so direction should be strongly positive
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assert!(result.direction > 0.5);
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}
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#[test]
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fn test_ensemble_custom_weights() {
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let adapters: Vec<Box<dyn ModelInferenceAdapter>> = vec![
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Box::new(DummyAdapter { name: "A".into(), direction: 1.0, confidence: 1.0, ready: true }),
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Box::new(DummyAdapter { name: "B".into(), direction: -1.0, confidence: 1.0, ready: true }),
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];
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let mut ensemble = InferenceEnsemble::new(adapters);
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// Give model A 3x weight of model B
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ensemble.set_weight("A", 0.75);
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ensemble.set_weight("B", 0.25);
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let fv = FeatureVector { values: vec![0.0; 51], timestamp: 0 };
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let result = ensemble.predict(&fv).unwrap();
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// (1.0 * 0.75 * 1.0 + (-1.0) * 0.25 * 1.0) = 0.5 (before normalization)
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assert!(result.direction > 0.3, "A's weight should dominate");
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}
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}
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```
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**Step 2: Run tests to verify they fail**
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Run: `SQLX_OFFLINE=true cargo test -p ml --lib ensemble::inference_ensemble -- --nocapture 2>&1 | tail -5`
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Expected: Compilation error — `InferenceEnsemble` not defined
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**Step 3: Write minimal implementation**
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```rust
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//! Lightweight synchronous ensemble that aggregates ModelInferenceAdapter predictions.
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use std::collections::HashMap;
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use crate::ensemble::inference_adapter::{
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EnsemblePrediction, FeatureVector, ModelInferenceAdapter, PredictionMeta,
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};
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use crate::{MLError, MLResult};
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/// Synchronous ensemble coordinator wrapping N model adapters.
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///
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/// Collects predictions from all ready adapters and produces a
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/// confidence-weighted consensus direction.
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pub struct InferenceEnsemble {
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adapters: Vec<Box<dyn ModelInferenceAdapter>>,
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weights: HashMap<String, f64>,
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}
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impl InferenceEnsemble {
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pub fn new(adapters: Vec<Box<dyn ModelInferenceAdapter>>) -> Self {
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Self {
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adapters,
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weights: HashMap::new(),
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}
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}
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/// Set a custom weight for a model (by name). Default weight is 1.0.
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pub fn set_weight(&mut self, model_name: &str, weight: f64) {
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self.weights.insert(model_name.to_string(), weight);
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}
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/// Predict from all ready models and aggregate.
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pub fn predict(&self, features: &FeatureVector) -> MLResult<EnsemblePrediction> {
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let start = std::time::Instant::now();
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let mut predictions = Vec::new();
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for adapter in &self.adapters {
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if !adapter.is_ready() {
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continue;
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}
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match adapter.predict(features) {
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Ok(pred) => predictions.push(pred),
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Err(e) => {
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tracing::warn!(model = adapter.model_name(), error = %e, "Adapter prediction failed, skipping");
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}
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}
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}
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if predictions.is_empty() {
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return Err(MLError::InferenceError("No ready models in ensemble".into()));
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}
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// Confidence-weighted aggregation
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let mut weighted_direction = 0.0;
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let mut total_weight = 0.0;
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for pred in &predictions {
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let model_weight = self.weights.get(&pred.model_name).copied().unwrap_or(1.0);
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let w = model_weight * pred.confidence;
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weighted_direction += pred.direction * w;
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total_weight += w;
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}
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let direction = if total_weight > 0.0 {
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(weighted_direction / total_weight).clamp(-1.0, 1.0)
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} else {
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0.0
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};
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let confidence = if !predictions.is_empty() {
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let avg_conf: f64 = predictions.iter().map(|p| p.confidence).sum::<f64>()
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/ predictions.len() as f64;
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avg_conf.clamp(0.0, 1.0)
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} else {
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0.0
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};
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let latency_us = start.elapsed().as_micros() as u64;
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let model_names: Vec<String> = predictions.iter().map(|p| p.model_name.clone()).collect();
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Ok(EnsemblePrediction {
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model_name: format!("ENSEMBLE({})", model_names.join("+")),
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direction,
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confidence,
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metadata: PredictionMeta {
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latency_us,
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quantiles: None,
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attention_weights: None,
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q_values: None,
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},
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})
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}
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/// Number of adapters currently ready.
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pub fn ready_count(&self) -> usize {
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self.adapters.iter().filter(|a| a.is_ready()).count()
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}
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}
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```
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**Step 4: Run tests to verify they pass**
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Run: `SQLX_OFFLINE=true cargo test -p ml --lib ensemble::inference_ensemble -- --nocapture`
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Expected: 3 tests pass
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**Step 5: Commit**
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```bash
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git add ml/src/ensemble/inference_ensemble.rs ml/src/ensemble/mod.rs
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git commit -m "feat(ensemble): add InferenceEnsemble coordinator with confidence-weighted aggregation"
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```
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---
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### Task 7: Integration test — 4-model ensemble prediction
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Create an integration test that constructs all 4 real adapters (DQN, PPO, Mamba2, TFT) with random weights, feeds feature vectors, and verifies the ensemble produces bounded predictions.
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**Files:**
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- Create: `ml/tests/ensemble_inference_integration_test.rs`
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**Step 1: Write the test**
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```rust
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//! Integration test: 4-model InferenceEnsemble
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//!
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//! Creates DQN, PPO, Mamba2, and TFT adapters with default/small configs,
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//! feeds feature vectors through the ensemble, and verifies output bounds.
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#![allow(unused_crate_dependencies)]
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use ml::dqn::{DQNConfig, DQN};
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use ml::ensemble::adapters::{
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DqnInferenceAdapter, Mamba2InferenceAdapter, PpoInferenceAdapter, TftInferenceAdapter,
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};
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use ml::ensemble::inference_adapter::FeatureVector;
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use ml::ensemble::inference_ensemble::InferenceEnsemble;
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use ml::mamba::Mamba2Config;
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use ml::ppo::PPOConfig;
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use ml::tft::TFTConfig;
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#[test]
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fn test_4_model_ensemble_prediction() {
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// Create DQN adapter
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let dqn_config = DQNConfig::default();
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let dqn = DQN::new(&dqn_config).expect("DQN creation");
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let dqn_adapter = DqnInferenceAdapter::new(dqn);
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// Create PPO adapter
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let ppo_config = PPOConfig::default();
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let ppo = ml::ppo::PPO::new(&ppo_config).expect("PPO creation");
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let ppo_adapter = PpoInferenceAdapter::new(ppo);
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// Create Mamba2 adapter with small config
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let mamba2_config = Mamba2Config {
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d_model: 64,
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d_state: 16,
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d_head: 16,
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num_heads: 2,
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expand: 2,
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num_layers: 1,
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max_seq_len: 8,
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..Default::default()
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};
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let mamba2_adapter = Mamba2InferenceAdapter::new(mamba2_config, 4)
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.expect("Mamba2 adapter creation");
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// Create TFT adapter with small config
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let tft_config = TFTConfig {
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input_dim: 20,
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hidden_dim: 32,
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num_heads: 2,
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num_layers: 1,
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prediction_horizon: 5,
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sequence_length: 4,
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num_quantiles: 9,
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num_static_features: 6,
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num_known_features: 6,
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num_unknown_features: 8,
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dropout_rate: 0.0,
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..Default::default()
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};
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let tft_adapter = TftInferenceAdapter::new(tft_config, 4)
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.expect("TFT adapter creation");
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// Build ensemble
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let adapters: Vec<Box<dyn ml::ensemble::inference_adapter::ModelInferenceAdapter>> = vec![
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Box::new(dqn_adapter),
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Box::new(ppo_adapter),
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Box::new(mamba2_adapter),
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Box::new(tft_adapter),
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];
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let ensemble = InferenceEnsemble::new(adapters);
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// Feed 5 feature vectors (enough to fill Mamba2 + TFT buffers at seq_len=4)
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for i in 0..5 {
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let fv = FeatureVector {
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values: vec![0.1 * (i as f64 + 1.0); 51],
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timestamp: 1_700_000_000 + i,
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};
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let result = ensemble.predict(&fv);
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// DQN and PPO are always ready (single-step models)
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// Mamba2 and TFT need 4 vectors to fill their buffers
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assert!(result.is_ok(), "Ensemble prediction should not error: {:?}", result.err());
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let pred = result.unwrap();
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assert!(pred.direction >= -1.0 && pred.direction <= 1.0,
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"direction {} out of [-1,1]", pred.direction);
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assert!(pred.confidence >= 0.0 && pred.confidence <= 1.0,
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"confidence {} out of [0,1]", pred.confidence);
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}
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// After 5 FVs, all 4 models should be ready
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assert_eq!(ensemble.ready_count(), 4, "All 4 models should be ready after 5 FVs");
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}
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```
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**Step 2: Run test**
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|
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Run: `SQLX_OFFLINE=true cargo test -p ml --test ensemble_inference_integration_test -- --nocapture`
|
|
Expected: 1 test passes
|
|
|
|
Note: This test may need adjustments to the import paths depending on how DQN/PPO constructors are exposed. If DQN/PPO adapters need the model passed differently, adjust accordingly. The key pattern is: construct model → wrap in adapter → add to ensemble.
|
|
|
|
**Step 3: Commit**
|
|
|
|
```bash
|
|
git add ml/tests/ensemble_inference_integration_test.rs
|
|
git commit -m "test(ensemble): add 4-model inference ensemble integration test"
|
|
```
|
|
|
|
---
|
|
|
|
## Phase 3: Hyperopt Campaign Runner
|
|
|
|
### Task 8: Create hyperopt campaign CLI runner
|
|
|
|
Build a CLI entrypoint that runs a hyperopt campaign for a given model type.
|
|
|
|
**Files:**
|
|
- Create: `ml/src/hyperopt/campaign.rs`
|
|
- Modify: `ml/src/hyperopt/mod.rs` (add `pub mod campaign;`)
|
|
|
|
**Step 1: Write the failing test**
|
|
|
|
In `ml/src/hyperopt/campaign.rs`:
|
|
|
|
```rust
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn test_campaign_config_defaults() {
|
|
let config = CampaignConfig::dqn_default();
|
|
assert_eq!(config.model_type, ModelType::DQN);
|
|
assert_eq!(config.num_trials, 50);
|
|
assert!(config.max_batch_size > 0);
|
|
}
|
|
|
|
#[test]
|
|
fn test_campaign_config_ppo() {
|
|
let config = CampaignConfig::ppo_default();
|
|
assert_eq!(config.model_type, ModelType::PPO);
|
|
assert_eq!(config.num_trials, 30);
|
|
}
|
|
|
|
#[test]
|
|
fn test_results_dir_creation() {
|
|
let config = CampaignConfig::dqn_default();
|
|
let dir = config.results_dir();
|
|
assert!(dir.starts_with("ml/hyperopt_results/dqn/"));
|
|
}
|
|
}
|
|
```
|
|
|
|
**Step 2: Write implementation**
|
|
|
|
```rust
|
|
//! Hyperopt campaign runner — configures and executes multi-trial optimization.
|
|
|
|
use std::path::PathBuf;
|
|
|
|
/// Model type for campaign targeting.
|
|
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
|
pub enum ModelType {
|
|
DQN,
|
|
PPO,
|
|
}
|
|
|
|
/// Campaign configuration.
|
|
#[derive(Debug, Clone)]
|
|
pub struct CampaignConfig {
|
|
pub model_type: ModelType,
|
|
pub num_trials: usize,
|
|
pub data_dir: PathBuf,
|
|
pub max_batch_size: usize,
|
|
pub early_stopping_eta: usize,
|
|
pub max_epochs_per_trial: usize,
|
|
pub results_base_dir: PathBuf,
|
|
}
|
|
|
|
impl CampaignConfig {
|
|
pub fn dqn_default() -> Self {
|
|
Self {
|
|
model_type: ModelType::DQN,
|
|
num_trials: 50,
|
|
data_dir: PathBuf::from("test_data/real/databento/ml_training"),
|
|
max_batch_size: 230, // RTX 3050 Ti 4GB VRAM limit
|
|
early_stopping_eta: 3,
|
|
max_epochs_per_trial: 81,
|
|
results_base_dir: PathBuf::from("ml/hyperopt_results"),
|
|
}
|
|
}
|
|
|
|
pub fn ppo_default() -> Self {
|
|
Self {
|
|
model_type: ModelType::PPO,
|
|
num_trials: 30,
|
|
data_dir: PathBuf::from("test_data/real/databento/ml_training"),
|
|
max_batch_size: 230,
|
|
early_stopping_eta: 3,
|
|
max_epochs_per_trial: 81,
|
|
results_base_dir: PathBuf::from("ml/hyperopt_results"),
|
|
}
|
|
}
|
|
|
|
/// Generate a timestamped results directory path.
|
|
pub fn results_dir(&self) -> String {
|
|
let model_name = match self.model_type {
|
|
ModelType::DQN => "dqn",
|
|
ModelType::PPO => "ppo",
|
|
};
|
|
let timestamp = chrono::Utc::now().format("%Y%m%d_%H%M%S");
|
|
format!("{}/{}/{}", self.results_base_dir.display(), model_name, timestamp)
|
|
}
|
|
}
|
|
```
|
|
|
|
**Step 3: Run tests**
|
|
|
|
Run: `SQLX_OFFLINE=true cargo test -p ml --lib hyperopt::campaign -- --nocapture`
|
|
Expected: 3 tests pass
|
|
|
|
**Step 4: Commit**
|
|
|
|
```bash
|
|
git add ml/src/hyperopt/campaign.rs ml/src/hyperopt/mod.rs
|
|
git commit -m "feat(hyperopt): add campaign configuration with DQN/PPO defaults"
|
|
```
|
|
|
|
---
|
|
|
|
### Task 9: Campaign execution and results persistence
|
|
|
|
Implement the actual trial loop that drives the hyperopt campaign.
|
|
|
|
**Files:**
|
|
- Modify: `ml/src/hyperopt/campaign.rs` (add `run_campaign` async fn)
|
|
|
|
**Step 1: Write the test (smoke test with 2 trials)**
|
|
|
|
```rust
|
|
#[tokio::test]
|
|
async fn test_campaign_smoke_2_trials() {
|
|
let data_dir = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
|
|
.parent()
|
|
.unwrap()
|
|
.join("test_data/real/databento/ml_training_small");
|
|
|
|
if !data_dir.exists() {
|
|
eprintln!("Skipping: test data not found at {}", data_dir.display());
|
|
return;
|
|
}
|
|
|
|
let mut config = CampaignConfig::dqn_default();
|
|
config.num_trials = 2;
|
|
config.max_epochs_per_trial = 5;
|
|
config.data_dir = data_dir;
|
|
|
|
let results = run_campaign(config).await.expect("Campaign should complete");
|
|
assert_eq!(results.trials_completed, 2);
|
|
assert!(results.best_loss.is_finite());
|
|
}
|
|
```
|
|
|
|
**Step 2: Implement `run_campaign`**
|
|
|
|
This function should:
|
|
1. Create the results directory
|
|
2. Loop for `num_trials` iterations
|
|
3. Sample random DQNParams using `ParameterSpace` trait
|
|
4. Train for `max_epochs_per_trial` epochs
|
|
5. Use `EarlyStoppingObserver` for pruning
|
|
6. Track best params and loss
|
|
7. Save best checkpoint and params JSON to results dir
|
|
|
|
**Step 3: Run test**
|
|
|
|
Run: `SQLX_OFFLINE=true cargo test -p ml campaign_smoke -- --nocapture`
|
|
Expected: Passes (may take 30-60s for 2 trials x 5 epochs)
|
|
|
|
**Step 4: Commit**
|
|
|
|
```bash
|
|
git add ml/src/hyperopt/campaign.rs
|
|
git commit -m "feat(hyperopt): implement campaign runner with trial loop and results persistence"
|
|
```
|
|
|
|
---
|
|
|
|
### Task 10: Post-campaign automated validation
|
|
|
|
After hyperopt finds best params, automatically run them through the ValidationHarness.
|
|
|
|
**Files:**
|
|
- Modify: `ml/src/hyperopt/campaign.rs` (add `validate_best` fn)
|
|
|
|
**Step 1: Write the test**
|
|
|
|
```rust
|
|
#[test]
|
|
fn test_campaign_results_serialization() {
|
|
let results = CampaignResults {
|
|
trials_completed: 10,
|
|
best_loss: 0.42,
|
|
best_params_json: "{\"lr\": 0.001}".to_string(),
|
|
best_checkpoint_path: Some("path/to/best.safetensors".into()),
|
|
total_time_seconds: 120.0,
|
|
};
|
|
let json = serde_json::to_string(&results).expect("serialize");
|
|
let back: CampaignResults = serde_json::from_str(&json).expect("deserialize");
|
|
assert_eq!(back.trials_completed, 10);
|
|
}
|
|
```
|
|
|
|
**Step 2: Implement `CampaignResults` struct with serde**
|
|
|
|
**Step 3: Commit**
|
|
|
|
```bash
|
|
git add ml/src/hyperopt/campaign.rs
|
|
git commit -m "feat(hyperopt): add CampaignResults serialization and validation hook"
|
|
```
|
|
|
|
---
|
|
|
|
## Phase 4: Paper Trading Pipeline
|
|
|
|
### Task 11: Define `TradeSignal` type in ml crate
|
|
|
|
The ML-side signal that flows from ensemble to trading.
|
|
|
|
**Files:**
|
|
- Create: `ml/src/ensemble/signal.rs`
|
|
- Modify: `ml/src/ensemble/mod.rs`
|
|
|
|
**Step 1: Write test**
|
|
|
|
```rust
|
|
#[test]
|
|
fn test_trade_signal_from_ensemble_prediction() {
|
|
let pred = EnsemblePrediction {
|
|
model_name: "ENSEMBLE(DQN+PPO)".into(),
|
|
direction: 0.6,
|
|
confidence: 0.85,
|
|
metadata: PredictionMeta::default(),
|
|
};
|
|
let signal = TradeSignal::from_prediction(&pred, "6E.FUT");
|
|
assert_eq!(signal.action, TradeAction::Buy);
|
|
assert_eq!(signal.symbol, "6E.FUT");
|
|
assert!(signal.confidence > 0.8);
|
|
}
|
|
```
|
|
|
|
**Step 2: Implement**
|
|
|
|
```rust
|
|
/// Trade action derived from ensemble prediction.
|
|
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
|
pub enum TradeAction {
|
|
Buy,
|
|
Sell,
|
|
Hold,
|
|
}
|
|
|
|
/// Signal emitted by the ML ensemble for the trading engine.
|
|
#[derive(Debug, Clone)]
|
|
pub struct TradeSignal {
|
|
pub action: TradeAction,
|
|
pub symbol: String,
|
|
pub confidence: f64,
|
|
pub direction: f64,
|
|
pub timestamp_us: i64,
|
|
}
|
|
|
|
impl TradeSignal {
|
|
pub fn from_prediction(pred: &EnsemblePrediction, symbol: &str) -> Self {
|
|
let action = if pred.direction > 0.1 {
|
|
TradeAction::Buy
|
|
} else if pred.direction < -0.1 {
|
|
TradeAction::Sell
|
|
} else {
|
|
TradeAction::Hold
|
|
};
|
|
Self {
|
|
action,
|
|
symbol: symbol.to_string(),
|
|
confidence: pred.confidence,
|
|
direction: pred.direction,
|
|
timestamp_us: chrono::Utc::now().timestamp_micros(),
|
|
}
|
|
}
|
|
}
|
|
```
|
|
|
|
**Step 3: Run tests, commit**
|
|
|
|
```bash
|
|
git add ml/src/ensemble/signal.rs ml/src/ensemble/mod.rs
|
|
git commit -m "feat(ensemble): add TradeSignal type with Buy/Sell/Hold action mapping"
|
|
```
|
|
|
|
---
|
|
|
|
### Task 12: PaperBroker — simulated fill engine
|
|
|
|
Lives in the ml crate for now (no cross-crate dependency needed for paper trading).
|
|
|
|
**Files:**
|
|
- Create: `ml/src/paper_trading/mod.rs`
|
|
- Create: `ml/src/paper_trading/broker.rs`
|
|
- Modify: `ml/src/lib.rs` (add `pub mod paper_trading;`)
|
|
|
|
**Step 1: Write test**
|
|
|
|
```rust
|
|
#[test]
|
|
fn test_paper_broker_buy_and_sell() {
|
|
let mut broker = PaperBroker::new(100_000.0, 0.0001); // $100k, 1bps slippage
|
|
let fill = broker.execute_signal(&TradeSignal {
|
|
action: TradeAction::Buy,
|
|
symbol: "6E.FUT".into(),
|
|
confidence: 0.9,
|
|
direction: 0.5,
|
|
timestamp_us: 0,
|
|
}, 1.0850); // current price
|
|
|
|
assert!(fill.is_some());
|
|
let fill = fill.unwrap();
|
|
assert!(fill.fill_price > 1.0850); // slippage applied
|
|
assert_eq!(broker.position_size(), 1); // 1 contract long
|
|
}
|
|
```
|
|
|
|
**Step 2: Implement PaperBroker**
|
|
|
|
- Track cash, positions, commission
|
|
- `execute_signal` takes `TradeSignal` + current market price → `Option<Fill>`
|
|
- Apply slippage: `fill_price = price * (1 + slippage * direction_sign)`
|
|
- Commission per trade (configurable, default $2.50 per contract)
|
|
- Track P&L: `unrealized_pnl(current_price)` and `realized_pnl()`
|
|
|
|
**Step 3: Run tests, commit**
|
|
|
|
```bash
|
|
git add ml/src/paper_trading/ ml/src/lib.rs
|
|
git commit -m "feat(paper_trading): add PaperBroker with simulated fills and position tracking"
|
|
```
|
|
|
|
---
|
|
|
|
### Task 13: PnL Tracker — cumulative returns and Sharpe
|
|
|
|
**Files:**
|
|
- Create: `ml/src/paper_trading/pnl_tracker.rs`
|
|
- Modify: `ml/src/paper_trading/mod.rs`
|
|
|
|
**Step 1: Write test**
|
|
|
|
```rust
|
|
#[test]
|
|
fn test_pnl_tracker_sharpe_calculation() {
|
|
let mut tracker = PnLTracker::new(252); // 252-bar rolling window
|
|
// Simulate 10 bars of small positive returns
|
|
for i in 0..10 {
|
|
tracker.record_return(0.001 + 0.0001 * (i as f64));
|
|
}
|
|
let sharpe = tracker.rolling_sharpe();
|
|
assert!(sharpe.is_finite());
|
|
assert!(sharpe > 0.0, "Positive returns should yield positive Sharpe");
|
|
|
|
let drawdown = tracker.max_drawdown();
|
|
assert!(drawdown >= 0.0);
|
|
assert!(drawdown <= 1.0);
|
|
}
|
|
```
|
|
|
|
**Step 2: Implement**
|
|
|
|
- Ring buffer of returns (configurable window, default 252)
|
|
- `rolling_sharpe()`: `mean / std * sqrt(252)`
|
|
- `max_drawdown()`: track peak equity, compute max drop
|
|
- `cumulative_return()`: product of `(1 + r)` - 1
|
|
|
|
**Step 3: Run tests, commit**
|
|
|
|
```bash
|
|
git add ml/src/paper_trading/pnl_tracker.rs ml/src/paper_trading/mod.rs
|
|
git commit -m "feat(paper_trading): add PnLTracker with rolling Sharpe and max drawdown"
|
|
```
|
|
|
|
---
|
|
|
|
### Task 14: Paper trading pipeline integration test
|
|
|
|
End-to-end test: replay historical OHLCV data → feature extraction → ensemble → signals → paper broker → P&L.
|
|
|
|
**Files:**
|
|
- Create: `ml/tests/paper_trading_integration_test.rs`
|
|
|
|
**Step 1: Write test**
|
|
|
|
```rust
|
|
//! Paper trading pipeline integration test.
|
|
//!
|
|
//! Replays synthetic OHLCV data through the full pipeline:
|
|
//! Feature extraction → Ensemble → TradeSignal → PaperBroker → PnL
|
|
|
|
#![allow(unused_crate_dependencies)]
|
|
|
|
use ml::ensemble::adapters::{DqnInferenceAdapter, PpoInferenceAdapter};
|
|
use ml::ensemble::inference_adapter::FeatureVector;
|
|
use ml::ensemble::inference_ensemble::InferenceEnsemble;
|
|
use ml::ensemble::signal::TradeSignal;
|
|
use ml::paper_trading::broker::PaperBroker;
|
|
use ml::paper_trading::pnl_tracker::PnLTracker;
|
|
|
|
#[test]
|
|
fn test_paper_trading_pipeline_synthetic_data() {
|
|
// Build ensemble with 2 models (DQN + PPO for speed)
|
|
// ... construct adapters ...
|
|
let ensemble = InferenceEnsemble::new(adapters);
|
|
|
|
let mut broker = PaperBroker::new(100_000.0, 0.0001);
|
|
let mut tracker = PnLTracker::new(252);
|
|
|
|
// Simulate 100 bars of synthetic price data
|
|
let mut price = 1.0850;
|
|
for i in 0..100 {
|
|
// Synthetic feature vector
|
|
let fv = FeatureVector {
|
|
values: vec![price; 51],
|
|
timestamp: 1_700_000_000 + i,
|
|
};
|
|
|
|
if let Ok(pred) = ensemble.predict(&fv) {
|
|
let signal = TradeSignal::from_prediction(&pred, "6E.FUT");
|
|
broker.execute_signal(&signal, price);
|
|
}
|
|
|
|
// Random walk price
|
|
price += 0.0001 * ((i % 7) as f64 - 3.0);
|
|
|
|
// Record bar return
|
|
let pnl = broker.unrealized_pnl(price);
|
|
tracker.record_equity(100_000.0 + pnl);
|
|
}
|
|
|
|
// Assertions
|
|
let sharpe = tracker.rolling_sharpe();
|
|
assert!(sharpe.is_finite(), "Sharpe should be finite");
|
|
|
|
let drawdown = tracker.max_drawdown();
|
|
assert!(drawdown.is_finite(), "Drawdown should be finite");
|
|
assert!(drawdown <= 1.0, "Drawdown should be <= 100%");
|
|
|
|
println!("Pipeline test complete:");
|
|
println!(" Cumulative return: {:.4}%", tracker.cumulative_return() * 100.0);
|
|
println!(" Rolling Sharpe: {:.4}", sharpe);
|
|
println!(" Max drawdown: {:.4}%", drawdown * 100.0);
|
|
println!(" Final equity: ${:.2}", 100_000.0 + broker.unrealized_pnl(price));
|
|
}
|
|
```
|
|
|
|
**Step 2: Run test**
|
|
|
|
Run: `SQLX_OFFLINE=true cargo test -p ml --test paper_trading_integration_test -- --nocapture`
|
|
Expected: Passes, prints P&L summary
|
|
|
|
**Step 3: Commit**
|
|
|
|
```bash
|
|
git add ml/tests/paper_trading_integration_test.rs
|
|
git commit -m "test(paper_trading): add end-to-end pipeline integration test"
|
|
```
|
|
|
|
---
|
|
|
|
## Phase 5: Final Verification
|
|
|
|
### Task 15: Full compilation and test suite verification
|
|
|
|
**Step 1: Verify lib compilation**
|
|
|
|
Run: `SQLX_OFFLINE=true cargo check -p ml --lib`
|
|
Expected: No errors
|
|
|
|
**Step 2: Verify integration test compilation**
|
|
|
|
Run: `SQLX_OFFLINE=true cargo check -p ml --tests`
|
|
Expected: No errors
|
|
|
|
**Step 3: Run all lib tests**
|
|
|
|
Run: `SQLX_OFFLINE=true cargo test -p ml --lib 2>&1 | tail -30`
|
|
Expected: All pass
|
|
|
|
**Step 4: Run key integration tests**
|
|
|
|
Run:
|
|
```bash
|
|
SQLX_OFFLINE=true cargo test -p ml --test ensemble_inference_integration_test -- --nocapture
|
|
SQLX_OFFLINE=true cargo test -p ml --test paper_trading_integration_test -- --nocapture
|
|
SQLX_OFFLINE=true cargo test -p ml --test production_training_smoke_test -- --nocapture
|
|
```
|
|
Expected: All pass
|
|
|
|
**Step 5: Clippy check**
|
|
|
|
Run: `SQLX_OFFLINE=true cargo clippy -p ml -- -D warnings 2>&1 | tail -20`
|
|
Expected: No errors (warnings may exist but no denials)
|
|
|
|
---
|
|
|
|
## Constraints Checklist
|
|
|
|
- [ ] GPU: RTX 3050 Ti, 4GB VRAM — batch sizes clamped at 230
|
|
- [ ] `SQLX_OFFLINE=true` for all cargo commands
|
|
- [ ] Clippy denials: `unwrap_used`, `expect_used`, `panic`, `indexing_slicing` — safe patterns only
|
|
- [ ] No live broker integration (paper-trading only)
|
|
- [ ] Single asset (6E.FUT only)
|
|
|
|
## Summary
|
|
|
|
| Phase | Tasks | Key Deliverable |
|
|
|-------|-------|----------------|
|
|
| 1: Cleanup | 1-5 | Zero compilation errors across all targets |
|
|
| 2: Ensemble | 6-7 | `InferenceEnsemble::predict()` with 4 models |
|
|
| 3: Hyperopt | 8-10 | Campaign runner producing best params + checkpoint |
|
|
| 4: Paper Trading | 11-14 | Full pipeline: data → ensemble → signals → P&L |
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| 5: Verification | 15 | All tests green, clippy clean |
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