Knowledge Base

10 documents — trading research, strategies & system architecture

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Project Trading System Roadmap

Comprehensive implementation roadmap for Spectre trading system upgrades — 4 phases, 20+ items, prioritized by ROI per hour invested. Living document.

Spectre Trading System Upgrade Roadmap

Created 2026-03-31 from deep research across market microstructure, ML/AI, portfolio optimization, and crypto alpha sources.

Current State

  • 54 pairs active on Hyperliquid
  • 8 strategies (MR, TF, Breakout, FundingReversion, Momentum, VWAP, CascadeRecovery, OBVDivergence)
  • 14 external data sources collected but MOST ARE UNUSED in decisions
  • Order flow collected but observational only
  • Funding divergence z-scores computed but not consumed
  • ML feature extraction exists but model is mocked (weights.ml = 0.0)
  • Regime detection exists but not weighted in decision scoring

Phase 1: Wire Existing Signals (HIGHEST ROI — data already collected)

1A. Drawdown-Dependent Quadratic Sizing

  • What: Replace flat 0.75x drawdown penalty with quadratic curve
  • Formula: sizing_mult = 1.0 - 0.8 * (drawdown / criticalDD)^2, floor at 0.20
  • Why: Linear penalty is too aggressive at small drawdowns, too lenient at large ones. Quadratic is proportional to actual pain (Chan framework).
  • Impact: Better recovery from small drawdowns, faster circuit-breaking on large ones
  • Effort: 30 min | File: risk-manager.ts

1B. Wire Fear & Greed as Macro Regime Filter

  • What: Use Alternative.me Fear & Greed (already fetched) to modulate sizing
  • Logic: Extreme Fear (<20) = boost MR entries by 15%. Extreme Greed (>80) = reduce all entries by 15%. Normal = no change.
  • Why: Extreme readings are reliable contrarian signals over 1-4 week horizons (research-backed)
  • Impact: ~5-10% edge on entries during extreme sentiment
  • Effort: 30 min | File: signal-pipeline.ts

1C. Wire Funding Divergence Z-Score into Strategy Selection

  • What: Already computed every 60s (720h rolling z-score). Use as conviction modifier.
  • Logic: When z > 2.0 and signal is SHORT → boost 15%. When z < -2.0 and signal is LONG → boost 15%. When z opposes → reduce 10%.
  • Why: 11.4% average divergence between HL and Binance. Structural alpha.
  • Impact: Already partially integrated in signal-pipeline. Needs strengthening.
  • Effort: 30 min | File: signal-pipeline.ts (already has partial hook)

1D. Wire Deribit DVOL as Volatility Regime Input

  • What: BTC implied vol (DVOL) already fetched. Use to detect vol regime changes before realized vol catches up.
  • Logic: DVOL rising sharply = incoming volatility → tighten SLs, reduce size. DVOL extremely low = vol expansion coming → prepare for breakouts.
  • Why: Implied vol leads realized vol by hours to days
  • Impact: Earlier regime detection, fewer surprise liquidations
  • Effort: 45 min | File: regime-detector.ts, signal-pipeline.ts

1E. Wire CoinGlass Liquidation Data into TP Targeting

  • What: Already fetched. Use aggregate liquidation volumes to set TP levels.
  • Logic: Large liquidation clusters in our direction = extend TP (cascade incoming). Against our direction = tighten TP.
  • Impact: Already partially integrated (liquidation-aware TP). Needs full CoinGlass integration.
  • Effort: 30 min | Files: signal-pipeline.ts

Phase 2: Upgrade Existing Modules (HIGH ROI)

2A. Upgrade Order Flow to Full OFI (Cont-Kukanov-Stoikov)

  • What: Replace simple bid/ask ratio with academic OFI formula
  • Formula: Track L2 changes between snapshots, compute net flow per level
  • Why: OFI alone = Sharpe 3.68 (beats 54-feature ML models). Our current ratio is a crude approximation.
  • Impact: Dramatically better short-term direction prediction
  • Effort: 2-3 hours | File: order-flow.ts

2B. Add CVD (Cumulative Volume Delta) Tracking

  • What: Track aggressive buy vs sell volume from HL trades
  • Why: CVD divergence from price = strong reversal signal. HL tags buyer/seller directly.
  • Impact: New leading indicator for MR entries
  • Effort: 2 hours | New module or extend order-flow.ts

2C. Add Funding Rate Velocity

  • What: Track rate of change of funding, not just level
  • Why: Sudden spikes predict reversals better than sustained high funding
  • Impact: Sharper FundingReversion entries
  • Effort: 1 hour | File: funding-divergence-collector.ts

2D. Upgrade Regime Detection with HMM

  • What: 3-state Hidden Markov Model on returns + volatility
  • Why: HMM captures regime persistence better than threshold-based detection
  • Impact: More accurate regime classification, fewer false transitions
  • Effort: 4 hours | File: regime-detector.ts (+ new HMM library)

2E. Add VPIN (Volume-Synchronized Probability of Informed Trading)

  • What: Volume-bucketed buy/sell imbalance proxy
  • Why: Predicts extreme price movements and flash crashes. Regime filter: >0.7 = momentum, <0.3 = reversion
  • Impact: Better strategy routing
  • Effort: 2 hours | File: order-flow.ts or new module

Phase 3: New Data Sources (MEDIUM ROI — need API integration)

3A. Stablecoin Supply Monitoring

  • What: Track USDT/USDC supply changes, large mint/burn events
  • Why: 95.24% correlation with BTC price. $250M+ mints precede buying by 24-72h.
  • Source: CryptoQuant API (free tier) or on-chain monitoring
  • Effort: 2-3 hours | File: external-data.ts

3B. Coinbase Premium Index

  • What: BTC price on Coinbase vs global average
  • Why: Institutional demand indicator. Negative streaks precede drops by 1-3 days.
  • Source: CryptoQuant or computed from Coinbase + Binance APIs
  • Effort: 1-2 hours | File: external-data.ts

3C. BTC-SPX Macro Correlation Tracker

  • What: Rolling 30-day correlation between BTC and SPX/QQQ
  • Why: BTC-QQQ correlation hit 0.87. When corr > 0.8, SPX direction dominates.
  • Source: Yahoo Finance free API for SPX data
  • Effort: 2 hours | File: external-data.ts

3D. Options Max Pain Integration

  • What: Deribit BTC options max pain + OI concentration by strike
  • Why: Price gravitates toward max pain in final 2-3 days before expiry
  • Source: Deribit API (already have DVOL connection, extend)
  • Effort: 2-3 hours | File: external-data.ts

Phase 4: ML Pipeline (TRANSFORMATIVE — needs foundation)

4A. Data Collection Pipeline

  • What: Start storing all features + outcomes in DB for ML training
  • Why: Need 30+ days of feature snapshots before XGBoost can train
  • Impact: Foundation for everything in Phase 4
  • Effort: 3-4 hours | New: data collection job

4B. XGBoost Direction Model

  • What: Train gradient boosting on collected features for 15m direction prediction
  • Why: XGBoost beats LSTM consistently (55.9% accuracy, Sharpe 1.78)
  • Effort: 4-6 hours | Python: training script + ONNX export

4C. Enable ML Weight in Decision Engine

  • What: Switch weights.ml from 0.0 to 0.3-0.5 once model is trained
  • Why: ML captures non-linear relationships that rules-based strategies miss
  • Effort: 1 hour | File: decision-engine.ts

4D. CPCV Backtesting Upgrade

  • What: Replace 70/30 split with Combinatorial Purged Cross-Validation
  • Why: CPCV proven superior to walk-forward for anti-overfitting (de Prado)
  • Effort: 4-6 hours | Python: optimization pipeline upgrade

4E. DCC-GARCH Dynamic Correlation

  • What: Replace static correlation clusters with DCC-GARCH
  • Why: Crypto correlations are non-stationary. Static clusters fail during crashes.
  • Effort: 4-6 hours | Python library + integration

4F. LLM Sentiment Pipeline

  • What: Use CryptoPanic news (already fetched) with LLM for sentiment scoring
  • Why: GPT sentiment = Sharpe 3.05 on news data
  • Effort: 4-6 hours | File: llm-assessor.ts (extend)**

Priority Matrix

| Item | ROI/Hour | Effort | Impact | Dependencies |

|------|----------|--------|--------|-------------|

| 1A Quadratic DD | Very High | 30min | Medium | None |

| 1B Fear & Greed | Very High | 30min | Medium | None |

| 1C Funding Z-Score | Very High | 30min | High | None |

| 1D Deribit DVOL | High | 45min | Medium | None |

| 1E CoinGlass Liq | High | 30min | Medium | None |

| 2A Full OFI | Very High | 3h | Very High | None |

| 2B CVD | High | 2h | High | None |

| 2C Funding Velocity | High | 1h | Medium | None |

| 2D HMM Regime | Medium | 4h | High | None |

| 2E VPIN | Medium | 2h | Medium | None |

| 3A Stablecoin | Medium | 3h | High | API key |

| 3B Coinbase Premium | Medium | 2h | Medium | API |

| 4A Data Collection | Critical | 4h | Foundation | None |

| 4B XGBoost | High | 6h | Very High | 4A + 30 days data |

| 4F LLM Sentiment | Medium | 6h | High | API costs |

Implementation Order (Optimal Path)

  1. Phase 1 (all items) — 2-3 hours total, immediate impact
  2. 4A Data Collection — start ASAP, needs time to accumulate
  3. 2A Full OFI — single highest-impact upgrade
  4. 2B CVD — complements OFI
  5. 2C Funding Velocity — quick win
  6. 3A-3D New data sources — parallel work
  7. 2D HMM Regime — after data collection proves which regimes matter
  8. 4B-4F ML Pipeline — once 30+ days of features collected