ramseshk
acf3a556ec
Implemented regime detection in paper trader: - Rolling 30-tick volatility classifies market as LOW_VOL/NORMAL/HIGH_VOL - A-S fill probability adapts: 25% (low vol), 15% (normal), 8% (high vol) - HIGH_VOL with spreads >$30: skip trading (adverse selection protection) - Regime shown on dashboard header with color-coded badge Added docs/ADVANCED_STRATEGIES.md — comprehensive research covering: 1. Deep Learning LOB Prediction (Transformers/TLOB) 2. Latency Arbitrage in Fragmented Markets 3. Hawkes Process OFI Modeling 4. Cross-Chain MEV Arbitrage 5. Institutional Capital Flow Arbitrage (ETF flows) 6. Hybrid Transformer + Hawkes Fusion 7. Implementation Roadmap (Phase 1-4) All strategies referenced with papers from arXiv, SSRN, and empirical studies.
201 lines
7.8 KiB
Markdown
201 lines
7.8 KiB
Markdown
# Advanced Strategy Research
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> Forward-looking strategies for next-generation quantitative trading.
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> Implemented status: Regime-Switching A-S (live), others (research).
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---
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## 1. Regime-Switching Adaptive Market Making ✅ IMPLEMENTED
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**Status:** Live in paper trading engine.
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### Concept
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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.
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### Implementation
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- **Regime detection:** 30-tick rolling volatility classifies market as LOW_VOL (<15% ann.), NORMAL (15-60%), or HIGH_VOL (>60%)
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- **Adaptive behavior:**
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- LOW_VOL: fill probability 25%, aggressive spread capture
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- NORMAL: baseline 15% fill probability
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- HIGH_VOL: 8% fill probability, skip when spread >$30 (adverse selection protection)
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- Based on research showing static makers lost catastrophically during Oct 2025 flash crash while adaptive taker strategies profited from the directional move
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### Where to find it
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- `live/paper_trader.py` → `detect_regime()`, `simulate_avellaneda()`
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- Dashboard → Paper Trading tab → Avellaneda-Stoikov card shows regime-dependent behavior
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---
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## 2. Deep Learning LOB Prediction 🔬 RESEARCH
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### Concept
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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.
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### Architecture
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```
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Input: LOB snapshots (10 levels × 100 timesteps) → Transformer Encoder → Price Direction (UP/DOWN/FLAT)
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```
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### Key Papers
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- TLOB: A Novel Transformer Model with Dual Attention for Stock Price Trend Prediction (arXiv)
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- CatBoost on LOB features showed statistically significant alpha vs buy-and-hold
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### Implementation Path
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1. Collect tick-level LOB data from Hyperliquid (via WebSocket or API)
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2. Feature engineering: price levels, volumes, queue positions, order flow imbalance
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3. Train Transformer on GPU (A100 recommended)
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4. Deploy with ONNX Runtime for inference
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5. FPGA acceleration for sub-ms parsing → order placement pipeline
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### Challenges
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- GPU resources for training (estimated 40h on A100 for 1 month of data)
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- Real-time inference latency must be <1ms for HFT
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- Model drift requires periodic retraining
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---
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## 3. Latency Arbitrage in Fragmented Markets 🔬 RESEARCH
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### Concept
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Exploit microsecond/nanosecond price discrepancies across multiple trading venues. Modern equity markets have 12+ lit venues for the same security.
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### Economics
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- Cboe recently reduced latency from 50μs to 20μs — the arms race continues
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- Latency arbitrage contributes to tighter spreads and faster price discovery but may increase volatility
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- Success depends on physical proximity, not prediction accuracy
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### Infrastructure Requirements
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- Co-location in exchange data centers (NYSE Mahwah, CME Aurora, etc.)
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- FPGA-based market data parsing (sub-μs)
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- Direct market access to all relevant venues
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- Microwave networks between NJ and Chicago data centers
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### Regulatory
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- SEC Rule 611 (Order Protection Rule) requires best execution
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- Latency arbitrage is legal but faces increasing scrutiny as a "tax" on slower participants
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### Backtesting Caveat
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Must model venue-specific latencies and network delays; naive backtests overstate profitability by 100-1000x.
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---
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## 4. Hawkes Process Order Flow Imbalance 🔬 RESEARCH
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### Concept
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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.
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### Mathematical Foundation
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```
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λ(t) = μ + Σ α·e^(-β(t-t_i)) for t_i < t
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```
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Where μ is baseline intensity, α is self-excitation, β is decay rate.
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### Empirical Validation
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- Systematic study on NIFTY50 showed consistent predictive power (SSRN)
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- Multiple academic papers demonstrate OFI forecasting with Hawkes processes
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### Implementation
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- Continuous calibration of Hawkes parameters on incoming trade data
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- Compute predicted OFI → directional signal
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- Less computationally intensive than deep learning but more rigorous than simple OFI
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### Regime Sensitivity
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- Excels during trending/volatile periods (pronounced order clustering)
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- Underperforms in quiet, range-bound markets (Hawkes degenerates toward Poisson)
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---
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## 5. Cross-Chain MEV Arbitrage 🔬 RESEARCH
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### Concept
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Capture value from price discrepancies of the same asset across different blockchains. Execute transactions atomically across chains before competitors.
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### Market Size
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- One-year study (Sep 2023-Aug 2024) across 9 blockchains: 242,000 cross-chain arbitrages
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- Generated $9.4M profit on $466M volume
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### Types
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- **SIA (Sequence-Independent Arbitrage):** Trades on separate chains independently
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- **SDA (Sequence-Dependent Arbitrage):** Uses bridges to move funds, requires sequencing
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### Key Infrastructure
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- Jito Bundles (Solana): auction-based MEV extraction with ordered transaction bundles
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- Multiple chain RPCs and node operators
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- Smart contracts for atomic execution
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### Challenges
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- Cross-chain finality times vary (Solana ~0.4s, Ethereum ~12s)
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- Bridge security risks
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- Gas optimization critical for profitability
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---
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## 6. Institutional Capital Flow Arbitrage 🔬 RESEARCH
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### Concept
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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.
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### Signal Pipeline
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```
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SEC Filings (Form N-CEN) → ETF Flow Data → Anticipate Market Impact → Trade
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```
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### Timing
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- More medium-frequency (minutes/hours) than true HFT
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- Key source: regulatory filing data parsed before market digests it
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### Market Impact
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- Bitcoin correlation with S&P 500 increased significantly post-ETF
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- Gold correlation remained weak — BTC adopted as "tech-like" risk asset by institutions
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---
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## 7. Hybrid: Transformer + Hawkes Process 🔬 RESEARCH
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### Concept
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Fuse deep learning pattern recognition with point process rigor. Two independent models produce signals that are combined with dynamic regime-dependent weights.
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### Architecture
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```
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LOB Data ─┬─ Transformer → P(UP) ┐
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│ ├─ Weighted Fusion → Final Signal
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└─ Hawkes OFI → Direction ───┘
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```
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### Fusion Logic
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- Trending markets: weight Hawkes OFI higher (order clustering signal)
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- Sideways/chop: weight Transformer higher (subtle pattern detection)
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- Weights determined by regime detection module
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### Advantages
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- More robust than single-model approach
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- Better interpretability than pure deep learning
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- Mirrors real-world practice (teams combine multiple quant tools)
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### Complexity
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- Requires expertise in both deep learning (PyTorch/TF) and stochastic processes
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- Parallel inference of both models must stay within latency budget
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---
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## Implementation Roadmap
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| Phase | Timeline | Focus |
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|-------|----------|-------|
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| **Phase 1** ✅ | Current | Regime-switching A-S, basic strategies, dashboard |
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| **Phase 2** | Next | Hawkes OFI implementation, enhanced regime detection |
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| **Phase 3** | Future | Deep learning LOB predictor, cross-chain MEV |
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| **Phase 4** | Research | Hybrid models, ETF flow integration, latency arb |
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---
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## References
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- TLOB: Dual Attention Transformer for LOB (arXiv: 2024)
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- Hawkes Processes for High-Frequency Trading (SSRN)
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- Jito Bundles: MEV on Solana (Jito Labs, 2024)
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- Cross-Chain Arbitrage: 9 Blockchains, 242K Trades (arXiv: 2024)
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- Post-ETF Bitcoin: Flows, Correlations, and Market Structure (arXiv: 2024)
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- Flash Crash 2025: Market Maker vs Taker Performance (arXiv)
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- Rule 611 and Fragmentation Economics (QuestDB/DeltaStrategy)
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