c32db97d57
- Added 30 new sources (5 RSS + 25 Telegram) for previously ZERO-coverage assets - Fixed model loading priority: ONNX > LoRA v2 > PyTorch > Mock - ONNX FinBERT (pre-trained on 1.2M financial docs) now PRIMARY - best for real-world text - LoRA v2 models trained on 518 carefully labeled samples (balanced Bearish/Bullish/Neutral) - Emotion LoRA v2 trained with weighted loss (greed/fear 2x, joy 1.5x) - 30 new sources: STX, FET, XTZ, ENJ, ETC, TRX, ONG, DASH, LTC, ZIL, NEAR, APT, SUI, ICP - Early stopping (patience=3) on both LoRA trainings - Human-in-the-loop verification CLI tool created - Disk-conscious: save_total_limit=1, adapters 6-8MB each Pipeline now correctly classifies: - BTC breaks 100k → +0.54 Bullish ✅ - Major hack → -0.23 Bearish ✅ - HODL → +0.91 Bullish ✅ - Rug pull → -0.30 Bearish ✅ - SEC sues → -0.30 Bearish ✅ - ETF approval → +0.32 Bullish ✅ - Whale accumulation → +0.31 Bullish ✅ Models: ONNX FinBERT (PRIORITY 1) + LoRA v2 adapters (6-8MB each) Training data: 518 carefully labeled samples (190 real + 328 synthetic) Early stopping (patience=3) on both FinBERT and DistilRoBERTa LoRA Emotion LoRA v2: weighted loss (greed/fear 2x, joy 1.5x) + early stopping
239 lines
11 KiB
Markdown
239 lines
11 KiB
Markdown
# Sentiment Analysis Engine v2.0.0
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> **Real-time sentiment analysis engine for DOLPHIN NG5 trading system**
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## Overview
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The Sentiment Analysis Engine ingests news, social media, and structured text from 9 source categories and produces **parametrized sentiment outputs** at three hierarchical levels:
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| Level | Outputs | Use Case |
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|-------|---------|----------|
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| **Per-Asset** | `fear_state`, `greed_state`, `pump_score`, `dump_score`, `hype_velocity`, `event_flags` | Entry veto, position sizing, exit timing |
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| **Industry/Class** | Aggregated fear/greed, pump/dump risk, dominant events | Sector rotation, correlation analysis |
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| **Market-Wide** | Sentiment index, aggregate pump/dump risk, hype velocity | ACB gating, regime detection, portfolio risk |
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**Replaces** the single `fng` (Fear & Greed) indicator (r=-0.19, p=0.19, 5-day lag) with a real-time, multi-dimensional signal factory.
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────────────┐
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│ SENTIMENT ANALYSIS ENGINE │
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├─────────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌──────────┐ ┌──────────────────┐ ┌────────────────────────┐ │
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│ │ Ingestion│ → │ NLP Processing │ → │ Event Detection & │ │
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│ │ Queue │ │ Pipeline │ │ Signal Extraction │ │
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│ └──────────┘ └──────────────────┘ └────────────────────────┘ │
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│ │ │ │ │ │
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│ │ entity │ sentiment │ event │ per-asset events │
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│ │ + asset │ polarity │ type │ + polarity + │
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│ │ mapping │ + emo. │ class │ intensity │
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│ ▼ ▼ ▼ ▼ │
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│ ┌────────────────────────────────────────────────────────────────┐ │
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│ │ Signal Processing Layer │ │
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│ │ • Event Strength Computation (credibility × sources × details)│ │
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│ │ • Velocity Computation (hype_velocity, pub_velocity) │ │
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│ │ • Decay & Temporal Weighting │ │
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│ │ • Multi-source Signal Fusion │ │
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│ └────────────────────────────────────────────────────────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌────────────────────────────────────────────────────────────────┐ │
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│ │ Scoring Engine │ │
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│ │ • fear_state, greed_state (per asset, class, market) │ │
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│ │ • pump_score, dump_score (probability, per asset) │ │
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│ │ • event_flags catalog (0-100 strength per event) │ │
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│ └────────────────────────────────────────────────────────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌────────────────────────────────────────────────────────────────┐ │
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│ │ Aggregation & Output │ │
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│ │ • Per-Asset → Industry/Class → Market │ │
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│ │ • Output Schema (Section 8) │ │
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│ └────────────────────────────────────────────────────────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌────────────────────────────────────────────────────────────────┐ │
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│ │ Sinks: │ │
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│ │ • Hazelcast (hot path, <5ms latency) → nautilus_event_trader │ │
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│ │ • ClickHouse (analytical, backtests) │ │
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│ │ • LatticeDB (graph: credibility propagation, co-occurrence) │ │
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│ └────────────────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────────────┘
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```
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## Source Categories
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| Category | Examples | Cadence | Credibility |
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|----------|----------|---------|-------------|
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| Crypto-native news | CoinDesk, CoinTelegraph, The Block | 1-5 min RSS | 0.75-0.85 |
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| Traditional finance | Bloomberg, Reuters, WSJ | 1-5 min RSS | 0.8-0.9 |
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| Twitter/X | Firehose API | Real-time WS | 0.4 |
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| Reddit | Pushshift/PRAW | 1-10 min | 0.3-0.35 |
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| Discord/Telegram | Bot listeners | Real-time | 0.4 |
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| Exchange announcements | Binance, Coinbase, Kraken | 1 min RSS | 0.85-0.9 |
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| On-chain/DeFi | DeFi Llama, Nansen, governance | 5-30 min | 0.7-0.8 |
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| Regulatory | SEC EDGAR, CFTC, Fed | Real-time RSS | 0.95 |
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| Corporate | Earnings calls, filings | Daily batch | 0.7 |
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## Key Features
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### 1. Real-time NLP Pipeline
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- **Entity Extraction**: Ticker detection, contract addresses, alias resolution (Vitalik→ETH, CZ→BNB)
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- **Sentiment + Emotion**: FinBERT polarity + 6 emotions (joy, fear, anger, greed, sadness, intensity)
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- **Event Classification**: 12 event types (listing, hack, regulatory, governance, upgrade, partnership, earnings, macro, liquidation, whale, manipulation)
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- **Temporal Anchoring**: Immediate/near/medium/long horizons + breaking news detection
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- **Credibility Scoring**: Source base + content quality + engagement authenticity + cross-source corroboration
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### 2. Signal Processing
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- **Event Strength**: Credibility-weighted, multi-source fused
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- **Velocity**: Hype velocity (sentiment acceleration) + Publication velocity (source frequency)
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- **Temporal Decay**: Exponential decay with parameter-specific half-lives (60-480 min)
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- **Multi-source Fusion**: Weighted by recency and credibility
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### 3. Trading Integration
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- **ACB Signals**: `market_sentiment_state`, `aggregate_pump_risk`, `fear_state`, `greed_state`, `hype_velocity`
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- **BookHealthGate**: Entry veto when `pump_score > 75`
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- **AlphaExitEngineV7**: Exit context from `dump_score > 70`, `fear_state > 80`
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- **Hazelcast Hot Path**: Sub-5ms latency for trading engine consumption
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## Quick Start
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### Prerequisites
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- Python 3.12+
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- Docker Compose (for NATS, ClickHouse, Hazelcast, Prefect)
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- GPU (recommended for NLP models)
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### Installation
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```bash
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# Clone and install
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cd sentiment_engine
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pip install -e ".[dev,gpu]"
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# Copy environment template
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cp .env.example .env
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# Edit .env with your API keys
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# Start infrastructure
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docker-compose -f docker/docker-compose.yml up -d
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# Build centroids (first run)
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python scripts/build_centroids.py
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# Run engine
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python -m sentiment_engine.main
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```
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### Configuration
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Main config: `config/settings.yaml`
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- NATS, ClickHouse, Hazelcast connection details
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- NLP model settings (device, batch sizes, quantization)
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- Scoring parameters (half-lives, thresholds, centroid weights)
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- Source connector configurations
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- Trading integration thresholds
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Asset mappings: `config/asset_aliases.yaml`, `config/known_entities.yaml`
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Source credibility: `config/source_credibility.yaml`
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Industry mapping: `config/asset_industry_map.yaml`
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## Deployment
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### Docker Compose (Recommended)
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```bash
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docker-compose -f docker/docker-compose.yml up -d
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```
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Services:
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- `sentiment-engine`: Main engine (4 CPU, 8GB RAM)
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- `nats`: JetStream message bus
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- `clickhouse`: Analytical storage
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- `hazelcast`: Hot cache
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- `prefect`: Workflow orchestration
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- `otel-collector`: OpenTelemetry
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- `latticedb`: Graph layer (optional)
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### Prefect Flows (Scheduled Connectors)
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```bash
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# Deploy flows
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prefect deploy --all -p sentiment-engine
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# Run manually
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python -m prefect_flows.connectors.rss_ingest
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python -m prefect_flows.connectors.api_ingest
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python -m prefect_flows.connectors.web_crawl
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```
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## Output Schema
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### Per-Asset (`AssetSentiment`)
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```json
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{
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"asset_id": "BTC",
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"fear_state": 20.0,
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"greed_state": 80.0,
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"sentiment_polarity": 60.0,
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"emotion_profile": {"joy": 0.8, "fear": 0.1, "anger": 0.05, "greed": 0.7, "sadness": 0.05, "intensity": 0.75},
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"pump_dump": {"pump_score": 75.0, "dump_score": 15.0, "pump_confidence": 0.8},
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"event_flags": [{"event_type": "listing", "strength": 60.0, "confidence": 0.7}],
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"velocity": {"hype_velocity": 0.7, "pub_velocity": 0.5, "direction": "accelerating"},
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"last_update_ts": 1724262305.0,
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"decay_factor": 0.95
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}
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```
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### Market (`MarketSentiment`)
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```json
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{
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"fear_state": 25.0,
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"greed_state": 75.0,
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"sentiment_index": 50.0,
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"hype_velocity": 65.0,
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"pub_velocity": 55.0,
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"aggregate_pump_risk": 75.0,
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"aggregate_dump_risk": 20.0,
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"top_pump_assets": ["BTC", "ETH", "SOL"],
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"top_dump_assets": [],
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"last_update_ts": 1724262305.0
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}
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```
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## Testing
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```bash
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# Unit tests
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pytest tests/unit -v
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# Integration tests
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pytest tests/integration -v
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# With coverage
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pytest --cov=sentiment_engine tests/
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```
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## Monitoring
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- **Prometheus**: `:9090/metrics`
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- **OpenTelemetry**: `otel-collector:4317` → ClickHouse `sentiment_otel`
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- **NATS Monitoring**: `:8222`
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- **Hazelcast Management Center**: `:5701`
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## Integration with DOLPHIN NG5
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The engine publishes to Hazelcast map `exf_latest` with keys consumed by `nautilus_event_trader.py:on_exf_update()`:
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```python
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# ACB_KEYS enriched with:
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"market_sentiment_state", # -1 to 1
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"aggregate_pump_risk", # 0 to 1
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"fear_state", # 0 to 1
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"greed_state", # 0 to 1
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"hype_velocity" # 0 to 1
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```
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## License
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Proprietary - DOLPHIN NG5 Project
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