foxhunt/AGENT_83_TLOB_TRAINING_BLOCKED.md

Agent 83: TLOB Production Training - BLOCKED

Date: 2025-10-14 Status: ❌ BLOCKED - Prerequisites NOT Met Priority: MEDIUM (long-running task, 3.5 days) Agent: 83 Wave: 160 Phase 2

---

🎯 Mission Summary

Original Task: Execute full 500-epoch TLOB transformer training with Level-2 order book data (~3.5 days GPU training).

Actual Status: CANNOT PROCEED - Multiple critical blockers identified.

---

🚫 Blocking Issues

1. ❌ Agent 82 Never Existed

Expected: Agent 82 (TLOB L2 Integration) completion Reality: No Agent 82 artifacts found in codebase

Search Results:

find . -name "*agent*82*" -o -name "*AGENT*82*"
# NO RESULTS

Implication: Agent 82 task was either skipped, merged into Agent 71, or never assigned.

---

2. ❌ Level-2 Order Book Data NOT Available

Expected: MBP-10 (Market By Price, 10 levels) data in test_data/real/databento/ml_training_l2/

Reality: Directory does not exist

ls -la test_data/real/databento/ml_training_l2
# ls: cannot access 'test_data/real/databento/ml_training_l2': No such file or directory

What We Have: 360 OHLCV DBN files (1-minute candle data, NOT Level-2 order book)

find test_data/real/databento -name "*.dbn" | wc -l
# 360

ls test_data/real/databento/ml_training/*.dbn | head -5
# ES.FUT_ohlcv-1m_2024-03-25.dbn
# ZN.FUT_ohlcv-1m_2024-02-09.dbn
# 6E.FUT_ohlcv-1m_2024-02-22.dbn
# NQ.FUT_ohlcv-1m_2024-01-15.dbn
# CL.FUT_ohlcv-1m_2024-01-04.dbn

Schema Mismatch:

• Required: mbp-10 (10 bid/ask price levels per snapshot)
• Available: ohlcv-1m (5 fields: open, high, low, close, volume)

---

3. ❌ TLOB Training Infrastructure Incomplete

Issue: While Agent 75 created TLOB trainer infrastructure, compilation fails due to upstream MAMBA-2 issues.

Compilation Errors:

error[E0061]: this function takes 2 arguments but 1 argument was supplied
   --> ml/src/benchmark/mamba2_benchmark.rs:299:23
    |
299 |         let model = Mamba2SSM::new(config)?;
    |                     ^^^^^^^^^^^^^^-------- argument #2 of type `&Device` is missing

error[E0061]: this function takes 2 arguments but 1 argument was supplied
   --> ml/src/benchmark/mamba2_benchmark.rs:424:9
    |
424 |         Mamba2SSM::new(config)
    |         ^^^^^^^^^^^^^^-------- argument #2 of type `&Device` is missing

Root Cause: MAMBA-2 API changed to require explicit device parameter, breaking downstream code.

Impact: Cannot compile ml crate, blocks TLOB training example compilation.

---

4. ❌ Agent 71 Tasks Incomplete

Agent 71 Status (from AGENT_71_STATUS_SUMMARY.md):

Task	Status	Blocker
Planning	✅ Complete	None
Code Written	✅ Complete (1,060+ lines)	None
API Version Fixed	⚠️ PENDING	DataBento API mismatch
Single-Day Test	⏳ NOT STARTED	API fix needed
Full Download	⏳ NOT STARTED	Test must pass first
TLOB Integration	⏳ NOT STARTED	Data must exist first

Key Findings:

1. ✅ TLOBDataLoader implemented (450 lines)
2. ✅ Download scripts created (610 lines)
3. ❌ DataBento API version mismatch (databento 0.17 → newer version)
4. ❌ No MBP-10 data downloaded ($12-$25 cost, 2-4 hours download)

Quote from Agent 71 Status:

Blocking Issue: ⚠️ DataBento API version mismatch (databento 0.17 → newer version)

Resolution Required:

• 2-4 hours to update examples to latest databento API
• Run single-day test to validate ($0.01-$0.05)
• Execute full 90-day download ($12-$25, 2-4 hours)

---

📊 Current Infrastructure Status

✅ What Works

1. TLOB Trainer Infrastructure (Agent 75):

   • ✅ TLOBTrainer implemented (637 lines)
   • ✅ Training example created (285 lines)
   • ✅ Hyperparameters struct
   • ✅ GPU/CPU device management
   • ✅ Checkpoint management
   • ✅ 4/4 unit tests passing

2. TLOB Data Loader (Agent 71):

   • ✅ TLOBDataLoader implemented (450 lines)
   • ✅ OrderBookSnapshot struct
   • ✅ MBP-10 parsing logic
   • ✅ 51-feature extraction integration
   • ✅ Train/val splitting

3. TLOB Feature Extraction (Existing):

   • ✅ TLOBFeatureExtractor (51 features)
   • ✅ Price level features (10 bid/ask)
   • ✅ Volume features
   • ✅ Microstructure features
   • ✅ Technical indicators

⚠️ What's Blocked

1. Level-2 Data Acquisition:

   • ⚠️ DataBento API version mismatch
   • ⚠️ No single-day test performed
   • ⚠️ No 90-day download executed
   • ⚠️ $12-$25 cost not yet incurred

2. TLOB Training:

   • ⚠️ No L2 data to train on
   • ⚠️ MAMBA-2 compilation errors block ml crate
   • ⚠️ Cannot compile train_tlob example

3. TLOB Inference:

   • ⚠️ No trained TLOB model available
   • ⚠️ Fallback prediction engine operational (rules-based)
   • ⚠️ Sub-50μs latency target unvalidated

---

🔄 Dependency Chain

Agent 71 (L2 Data Acquisition)
      ↓
  Fix DataBento API (2-4 hours)
      ↓
  Run Single-Day Test ($0.05, 30 min)
      ↓
  Execute 90-Day Download ($12-$25, 2-4 hours)
      ↓
  126M Order Book Snapshots Available
      ↓
Agent 82 (L2 Integration) ← **MISSING/SKIPPED**
      ↓
  Validate TLOBDataLoader with Real Data
      ↓
  Integration Tests (5 planned)
      ↓
Agent 83 (TLOB Training) ← **YOU ARE HERE**
      ↓
  Execute 500-Epoch Training (3.5 days)
      ↓
  Production TLOB Model Available

Current Position: Stuck at Agent 71 (incomplete), Agent 82 missing, Agent 83 blocked.

---

🛠️ Resolution Path

Option A: Complete Agent 71 Tasks (RECOMMENDED)

Priority: HIGH Duration: 5-9 hours total Cost: $12-$25 (DataBento data)

Step 1: Fix DataBento API (2-4 hours)

Issue: databento crate API changed from 0.17 → 0.21+

Solution:

cd /home/jgrusewski/Work/foxhunt

# Update Cargo.toml
sed -i 's/databento = "0.17"/databento = "0.21"/' ml/Cargo.toml
sed -i 's/dbn = "0.42"/dbn = "0.22"/' ml/Cargo.toml

# Update download examples (manual edits required)
# See AGENT_71_STATUS_SUMMARY.md Section: "Option 1: Update to Latest DataBento API"

# Test compilation
cargo check -p ml --examples

Expected Errors:

• GetRangeParamsBuilder::start() → Use start_date() instead
• AsyncDbnDecoder::len() → API removed
• DbnDecoder::metadata() → Use metadata().clone()
• decode_record_ref() → Use iterator-based API

Effort: 2-4 hours manual API migration

---

Step 2: Single-Day Test (30 min, $0.01-$0.05)

After API fix, run validation test:

cargo run -p ml --example download_l2_test --release

Expected Output:

✅ Downloaded 1 day MBP-10 data for ES.FUT
✅ Decoded 10,000-100,000 order book snapshots
✅ Validated 10 bid/ask levels per snapshot
📊 Cost: $0.02
📊 Extrapolated 90-day cost: $18.00

Success Criteria:

• ✅ File downloads successfully
• ✅ DBN parser reads MBP-10 records
• ✅ Record count in expected range
• ✅ Cost estimate reasonable (<$25 for 90 days)

---

Step 3: Full 90-Day Download (2-4 hours, $12-$25)

After test passes, execute full download:

cargo run -p ml --example download_l2_data --release

Parameters:

• Symbols: ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT
• Date range: 2024-01-02 to 2024-04-01 (90 days)
• Schema: mbp-10 (10 price levels)
• Expected files: 360 (90 days × 4 symbols)

Output:

✅ Downloaded 360 MBP-10 files
✅ 126M order book snapshots
✅ 10-20 GB compressed data
💰 Total cost: $18.50
📁 Saved to: test_data/real/databento/ml_training_l2/

Duration: 2-4 hours (API rate limiting: 10 req/min)

---

Step 4: Validate TLOBDataLoader (30 min)

After download completes, test data loading:

cargo run -p ml --example test_tlob_loader --release

Expected Output:

let loader = TLOBDataLoader::new(128, 51).await?;
let (train_data, val_data) = loader
    .load_sequences("test_data/real/databento/ml_training_l2", 0.9)
    .await?;

println!("Loaded {} training sequences", train_data.len());
// Expected: 100,000+ sequences

Success Criteria:

• ✅ Loads all 360 MBP-10 files
• ✅ Extracts 51 features per snapshot
• ✅ Creates 100K+ training sequences
• ✅ Train/val split (90/10) works
• ✅ Tensors created on GPU (if available)

---

Option B: Skip TLOB Training (Alternative)

Rationale: TLOB fallback engine already operational (11/11 tests passing)

Current Status:

• ✅ TLOB inference works (rules-based fallback)
• ✅ 51-feature extraction operational
• ✅ <100μs inference latency (target: <50μs)
• ✅ 11/11 integration tests passing

Implications:

• ⚠️ No neural network prediction (rules-based only)
• ⚠️ Sub-50μs latency unvalidated
• ⚠️ Cannot improve with training data

When This Makes Sense:

• If Level-2 data acquisition cost ($12-$25) is prohibitive
• If 3.5-day GPU training time is unacceptable
• If rules-based prediction performance sufficient
• If other models (DQN, PPO) provide sufficient signal

---

Option C: Wait for Agent 82 (Not Recommended)

Issue: Agent 82 doesn't exist and was likely skipped/merged

Evidence:

• No AGENT_82 artifacts in codebase
• Agent 71 → Agent 83 jump in task assignments
• TLOB integration work already in Agent 71's TLOBDataLoader

Conclusion: Agent 82 tasks were merged into Agent 71, not a separate agent.

---

📈 Training Estimates (If Data Available)

TLOB Training Performance (from Agent 75)

Configuration:

• Batch size: 16
• Sequence length: 128
• Model: 256d, 8 heads, 4 layers
• Dataset: 126M snapshots → 10,000 sequences (conservatively)

GPU Estimates (RTX 3050 Ti):

• Forward pass: ~5ms/batch
• Backward pass: ~10ms/batch
• Epoch time: ~10 minutes (625 batches)
• 500 epochs: ~83 hours (~3.5 days)
• VRAM usage: 2-4 GB (safe for 4GB GPU)
• GPU utilization: 40-50%

Expected Convergence:

• Loss: MSE <0.001 (target)
• MAE: <0.01 (mean absolute error)
• Checkpoints: 50 (every 10 epochs)

Production Inference (post-training):

• Latency: <50μs (target, estimated 30-40μs)
• Format: ONNX (for production deployment)
• Integration: Replace fallback engine

---

🎯 Recommendations

Priority 1: Complete Agent 71 (HIGH)

Action: Fix DataBento API, download L2 data, validate loader Duration: 5-9 hours Cost: $12-$25 Blocker: None (can start immediately)

Steps:

1. ✅ Fix DataBento API version mismatch (2-4 hours)
2. ✅ Run single-day test ($0.05, 30 min)
3. ✅ Execute 90-day download ($12-$25, 2-4 hours)
4. ✅ Validate TLOBDataLoader (30 min)

Expected Outcome: 126M order book snapshots available for TLOB training.

---

Priority 2: Fix MAMBA-2 Compilation (MEDIUM)

Action: Fix device parameter errors in MAMBA-2 benchmark Duration: 30-60 minutes Cost: $0 Blocker: None (independent of L2 data)

Files to Fix:

• ml/src/benchmark/mamba2_benchmark.rs (2 errors)
• Add missing &device parameter to Mamba2SSM::new() calls

Commands:

# Find all calls to Mamba2SSM::new
rg "Mamba2SSM::new" ml/src/benchmark/

# Fix manually (add &device parameter)
# Line 299: Mamba2SSM::new(config, &device)?
# Line 424: Mamba2SSM::new(config, &device)

# Test compilation
cargo build -p ml --lib --release

Expected Outcome: ml crate compiles, enables TLOB training example compilation.

---

Priority 3: Agent 83 TLOB Training (AFTER Priorities 1-2)

Action: Execute 500-epoch TLOB training Duration: 3.5 days GPU time Cost: $0 (local RTX 3050 Ti) Blocker: Agent 71 completion + MAMBA-2 fix

Command (after blockers resolved):

CUDA_VISIBLE_DEVICES=0 cargo run -p ml --example train_tlob --release -- \
  --epochs 500 \
  --learning-rate 0.0001 \
  --batch-size 16 \
  --seq-len 128 \
  --num-price-levels 10 \
  --d-model 256 \
  --num-heads 8 \
  --num-layers 4 \
  --output ml/trained_models/production/tlob_real_data \
  2>&1 | tee /tmp/tlob_production_training_$(date +%Y%m%d_%H%M%S).log

Monitoring (separate terminal):

watch -n 60 'nvidia-smi; tail -20 /tmp/tlob_production_training_*.log'

Expected Outcome: 50 checkpoints, MSE <0.001, production TLOB model ready.

---

📊 Success Criteria

Phase 1: Agent 71 Completion ✅

• ✅ DataBento API version fixed
• ✅ Single-day test passed ($0.05)
• ✅ 90-day download complete ($12-$25, 360 files)
• ✅ TLOBDataLoader validated (100K+ sequences)

Phase 2: Infrastructure Fix ✅

• ✅ MAMBA-2 compilation errors fixed
• ✅ ml crate builds successfully
• ✅ TLOB training example compiles

Phase 3: TLOB Training (Agent 83) ✅

• ✅ 500 epochs complete
• ✅ 50+ checkpoints generated
• ✅ MSE loss <0.001
• ✅ MAE convergence validated
• ✅ Zero NaN values
• ✅ GPU utilization 40-50%

---

📁 Files Referenced

Agent Reports

1. AGENT_71_STATUS_SUMMARY.md - L2 data acquisition status
2. AGENT_71_DATABENTO_L2_PLAN.md - Comprehensive 720-line plan
3. AGENT_71_HANDOFF.md - Next steps (mentions Agent 82, but doesn't exist)
4. AGENT_75_COMPLETION_SUMMARY.md - TLOB trainer implementation
5. AGENT_75_TLOB_TRAINER_DESIGN.md - 640-line architecture doc

Code Files

1. ml/src/trainers/tlob.rs - TLOB trainer (637 lines)
2. ml/examples/train_tlob.rs - Training example (285 lines)
3. ml/src/data_loaders/tlob_loader.rs - L2 data loader (450 lines)
4. ml/examples/download_l2_test.rs - Single-day test (230 lines)
5. ml/examples/download_l2_data.rs - Full downloader (380 lines)

Data Files

1. test_data/real/databento/ml_training/ - 360 OHLCV files (NOT L2)
2. test_data/real/databento/ml_training_l2/ - DOES NOT EXIST (needed)

---

🚫 Conclusion

Agent 83 Mission Status: ❌ BLOCKED - Cannot proceed until prerequisites met.

Critical Blockers:

1. ❌ Agent 82 (TLOB L2 Integration) never existed (likely merged into Agent 71)
2. ❌ Level-2 order book data NOT downloaded (Agent 71 incomplete)
3. ❌ DataBento API version mismatch blocks data acquisition
4. ❌ MAMBA-2 compilation errors block TLOB training example

Resolution Timeline:

• Agent 71 completion: 5-9 hours ($12-$25)
• MAMBA-2 fix: 30-60 minutes ($0)
• Agent 83 training: 3.5 days ($0)
• Total: 5-10 hours setup + 3.5 days training

Recommendation: Focus on Agent 71 completion first. TLOB training is a long-running task (3.5 days) that requires solid data infrastructure before starting.

Next Action: Resolve Agent 71 blockers (DataBento API fix + L2 data download).

---

Report Status: ✅ COMPLETE Agent: 83 Date: 2025-10-14 Priority: MEDIUM (blocked by HIGH priority Agent 71 tasks) Estimated Time to Unblock: 5-10 hours (Agent 71 completion + MAMBA-2 fix)