# Advanced Strategy Research

> Forward-looking strategies for next-generation quantitative trading.
> Implemented status: Regime-Switching A-S (live), others (research).

---

## 1. Regime-Switching Adaptive Market Making ✅ IMPLEMENTED

**Status:** Live in paper trading engine.

### Concept
The classic Avellaneda-Stoikov model assumes constant market parameters (volatility, liquidity). In reality, market regimes shift — a flash crash requires radically different behavior than a quiet Sunday.

### Implementation
- **Regime detection:** 30-tick rolling volatility classifies market as LOW_VOL (<15% ann.), NORMAL (15-60%), or HIGH_VOL (>60%)
- **Adaptive behavior:**
  - LOW_VOL: fill probability 25%, aggressive spread capture
  - NORMAL: baseline 15% fill probability
  - HIGH_VOL: 8% fill probability, skip when spread >$30 (adverse selection protection)
- Based on research showing static makers lost catastrophically during Oct 2025 flash crash while adaptive taker strategies profited from the directional move

### Where to find it
- `live/paper_trader.py` → `detect_regime()`, `simulate_avellaneda()`
- Dashboard → Paper Trading tab → Avellaneda-Stoikov card shows regime-dependent behavior

---

## 2. Deep Learning LOB Prediction 🔬 RESEARCH

### Concept
Transformer-based model (e.g., TLOB architecture) to forecast short-term mid-price movements from the full Limit Order Book. The collective structure of resting orders contains latent information about future liquidity provision and adverse selection risk.

### Architecture
```
Input: LOB snapshots (10 levels × 100 timesteps) → Transformer Encoder → Price Direction (UP/DOWN/FLAT)
```

### Key Papers
- TLOB: A Novel Transformer Model with Dual Attention for Stock Price Trend Prediction (arXiv)
- CatBoost on LOB features showed statistically significant alpha vs buy-and-hold

### Implementation Path
1. Collect tick-level LOB data from Hyperliquid (via WebSocket or API)
2. Feature engineering: price levels, volumes, queue positions, order flow imbalance
3. Train Transformer on GPU (A100 recommended)
4. Deploy with ONNX Runtime for inference
5. FPGA acceleration for sub-ms parsing → order placement pipeline

### Challenges
- GPU resources for training (estimated 40h on A100 for 1 month of data)
- Real-time inference latency must be <1ms for HFT
- Model drift requires periodic retraining

---

## 3. Latency Arbitrage in Fragmented Markets 🔬 RESEARCH

### Concept
Exploit microsecond/nanosecond price discrepancies across multiple trading venues. Modern equity markets have 12+ lit venues for the same security.

### Economics
- Cboe recently reduced latency from 50μs to 20μs — the arms race continues
- Latency arbitrage contributes to tighter spreads and faster price discovery but may increase volatility
- Success depends on physical proximity, not prediction accuracy

### Infrastructure Requirements
- Co-location in exchange data centers (NYSE Mahwah, CME Aurora, etc.)
- FPGA-based market data parsing (sub-μs)
- Direct market access to all relevant venues
- Microwave networks between NJ and Chicago data centers

### Regulatory
- SEC Rule 611 (Order Protection Rule) requires best execution
- Latency arbitrage is legal but faces increasing scrutiny as a "tax" on slower participants

### Backtesting Caveat
Must model venue-specific latencies and network delays; naive backtests overstate profitability by 100-1000x.

---

## 4. Hawkes Process Order Flow Imbalance 🔬 RESEARCH

### Concept
Model order arrivals as self-exciting point processes. Unlike Poisson (independent arrivals), Hawkes processes recognize trade clustering — a large buy triggers a cascade of further buying. This produces a mathematically rigorous Order Flow Imbalance (OFI) signal.

### Mathematical Foundation
```
λ(t) = μ + Σ α·e^(-β(t-t_i))  for t_i < t
```
Where μ is baseline intensity, α is self-excitation, β is decay rate.

### Empirical Validation
- Systematic study on NIFTY50 showed consistent predictive power (SSRN)
- Multiple academic papers demonstrate OFI forecasting with Hawkes processes

### Implementation
- Continuous calibration of Hawkes parameters on incoming trade data
- Compute predicted OFI → directional signal
- Less computationally intensive than deep learning but more rigorous than simple OFI

### Regime Sensitivity
- Excels during trending/volatile periods (pronounced order clustering)
- Underperforms in quiet, range-bound markets (Hawkes degenerates toward Poisson)

---

## 5. Cross-Chain MEV Arbitrage 🔬 RESEARCH

### Concept
Capture value from price discrepancies of the same asset across different blockchains. Execute transactions atomically across chains before competitors.

### Market Size
- One-year study (Sep 2023-Aug 2024) across 9 blockchains: 242,000 cross-chain arbitrages
- Generated $9.4M profit on $466M volume

### Types
- **SIA (Sequence-Independent Arbitrage):** Trades on separate chains independently
- **SDA (Sequence-Dependent Arbitrage):** Uses bridges to move funds, requires sequencing

### Key Infrastructure
- Jito Bundles (Solana): auction-based MEV extraction with ordered transaction bundles
- Multiple chain RPCs and node operators
- Smart contracts for atomic execution

### Challenges
- Cross-chain finality times vary (Solana ~0.4s, Ethereum ~12s)
- Bridge security risks
- Gas optimization critical for profitability

---

## 6. Institutional Capital Flow Arbitrage 🔬 RESEARCH

### Concept
The January 2024 Bitcoin ETF approval created a causal relationship between ETF flows and BTC price. Research shows ETF net flows explained ~95% of Bitcoin's price variance (R² ≈ 0.95) post-launch.

### Signal Pipeline
```
SEC Filings (Form N-CEN) → ETF Flow Data → Anticipate Market Impact → Trade
```

### Timing
- More medium-frequency (minutes/hours) than true HFT
- Key source: regulatory filing data parsed before market digests it

### Market Impact
- Bitcoin correlation with S&P 500 increased significantly post-ETF
- Gold correlation remained weak — BTC adopted as "tech-like" risk asset by institutions

---

## 7. Hybrid: Transformer + Hawkes Process 🔬 RESEARCH

### Concept
Fuse deep learning pattern recognition with point process rigor. Two independent models produce signals that are combined with dynamic regime-dependent weights.

### Architecture
```
LOB Data ─┬─ Transformer → P(UP)       ┐
          │                            ├─ Weighted Fusion → Final Signal
          └─ Hawkes OFI → Direction ───┘
```

### Fusion Logic
- Trending markets: weight Hawkes OFI higher (order clustering signal)
- Sideways/chop: weight Transformer higher (subtle pattern detection)
- Weights determined by regime detection module

### Advantages
- More robust than single-model approach
- Better interpretability than pure deep learning
- Mirrors real-world practice (teams combine multiple quant tools)

### Complexity
- Requires expertise in both deep learning (PyTorch/TF) and stochastic processes
- Parallel inference of both models must stay within latency budget

---

## Implementation Roadmap

| Phase | Timeline | Focus |
|-------|----------|-------|
| **Phase 1** ✅ | Current | Regime-switching A-S, basic strategies, dashboard |
| **Phase 2** | Next | Hawkes OFI implementation, enhanced regime detection |
| **Phase 3** | Future | Deep learning LOB predictor, cross-chain MEV |
| **Phase 4** | Research | Hybrid models, ETF flow integration, latency arb |

---

## References

- TLOB: Dual Attention Transformer for LOB (arXiv: 2024)
- Hawkes Processes for High-Frequency Trading (SSRN)
- Jito Bundles: MEV on Solana (Jito Labs, 2024)
- Cross-Chain Arbitrage: 9 Blockchains, 242K Trades (arXiv: 2024)
- Post-ETF Bitcoin: Flows, Correlations, and Market Structure (arXiv: 2024)
- Flash Crash 2025: Market Maker vs Taker Performance (arXiv)
- Rule 611 and Fragmentation Economics (QuestDB/DeltaStrategy)
