Chapter 68: LLM News Interpretation for Trading
Chapter 68: LLM News Interpretation for Trading
Overview
Large Language Models (LLMs) have revolutionized how we can interpret and extract actionable signals from financial news. This chapter explores using LLMs to analyze news articles, press releases, social media, and other textual data to generate trading signals. Unlike traditional sentiment analysis, LLM-based interpretation can understand nuanced context, identify causal relationships, and extract specific events that may impact asset prices.
Table of Contents
- Introduction
- Theoretical Foundation
- News Data Sources
- LLM Architecture for News Interpretation
- Feature Extraction Pipeline
- Signal Generation Strategies
- Application to Cryptocurrency Trading
- Implementation Strategy
- Risk Management
- Performance Metrics
- References
Introduction
Financial markets are driven by information. News events, announcements, and social sentiment can cause rapid price movements. Traditional approaches to news analysis rely on:
- Keyword matching: Simple but misses context
- Sentiment lexicons: Domain-specific but rigid
- ML classifiers: Require labeled data, limited generalization
Why LLMs for News Interpretation?
┌─────────────────────────────────────────────────────────────────────────┐│ News Interpretation Evolution │├─────────────────────────────────────────────────────────────────────────┤│ ││ Traditional Approach: LLM Approach: ││ ┌──────────────────┐ ┌──────────────────┐ ││ │ "Bitcoin drops" │ │ "Bitcoin drops" │ ││ │ ↓ │ │ ↓ │ ││ │ keyword: "drops" │ │ Context Analysis │ ││ │ ↓ │ │ ↓ │ ││ │ Signal: NEGATIVE │ │ Why dropping? │ ││ └──────────────────┘ │ - Whale selling? │ ││ │ - Regulatory? │ ││ │ - Technical? │ ││ │ ↓ │ ││ │ Nuanced Signal │ ││ │ with Confidence │ ││ └──────────────────┘ ││ ││ ┌─────────────────────────────────────────────────────────────────┐ ││ │ Example: "Ethereum upgrade delayed due to security concerns" │ ││ │ │ ││ │ Traditional: NEGATIVE (contains "delayed", "concerns") │ ││ │ LLM: MIXED - short-term negative (delay), long-term positive │ ││ │ (security priority shows maturity) │ ││ └─────────────────────────────────────────────────────────────────┘ ││ │└─────────────────────────────────────────────────────────────────────────┘Advantages of LLM-Based News Interpretation
| Aspect | Traditional NLP | LLM-Based |
|---|---|---|
| Context understanding | Limited | Deep contextual reasoning |
| Entity relationships | Basic NER | Complex relationship extraction |
| Temporal reasoning | Manual rules | Implicit understanding |
| Multi-hop inference | Not possible | Chain-of-thought reasoning |
| Zero-shot capability | None | Strong generalization |
| Domain adaptation | Requires retraining | Prompt engineering |
Theoretical Foundation
Information Processing in Markets
The Efficient Market Hypothesis (EMH) suggests prices reflect all available information. In practice:
$$P_{t+1} = P_t + \alpha \cdot I_{news} + \epsilon$$
Where:
- $P_t$ is the current price
- $I_{news}$ is the information content of news
- $\alpha$ is the market’s reaction coefficient
- $\epsilon$ is noise
LLM as Information Extractor
LLMs can be viewed as functions that map text to structured information:
$$f_{LLM}: \text{Text} \rightarrow {(\text{Entity}, \text{Event}, \text{Sentiment}, \text{Magnitude}, \text{Timeframe})}$$
Attention Mechanism for News
The transformer attention mechanism naturally handles:
┌─────────────────────────────────────────────────────────────────────────┐│ Attention in News Processing │├─────────────────────────────────────────────────────────────────────────┤│ ││ Input: "SEC approves Bitcoin ETF, marking historic regulatory shift" ││ ││ Token Attention Weights: ││ ┌─────┬──────────┬───────┬─────┬────────┬───────────┬───────────┐ ││ │ SEC │ approves │Bitcoin│ ETF │marking │ historic │regulatory │ ││ ├─────┼──────────┼───────┼─────┼────────┼───────────┼───────────┤ ││ │0.15 │ 0.25 │ 0.20 │0.18 │ 0.05 │ 0.08 │ 0.09 │ ││ └─────┴──────────┴───────┴─────┴────────┴───────────┴───────────┘ ││ ││ Key Information Extracted: ││ • Entity: Bitcoin, SEC, ETF ││ • Event: Approval (regulatory action) ││ • Sentiment: Positive (approval, historic) ││ • Magnitude: High (regulatory milestone) ││ • Timeframe: Immediate impact expected ││ │└─────────────────────────────────────────────────────────────────────────┘Event Classification Taxonomy
Financial Events Hierarchy:
├── Regulatory Events│ ├── Approvals (ETF, licenses)│ ├── Enforcement (fines, bans)│ ├── Legislation (new laws, amendments)│ └── Investigations (probes, audits)│├── Corporate Events│ ├── Partnerships (integrations, collaborations)│ ├── Acquisitions (M&A activity)│ ├── Product launches (mainnet, features)│ └── Leadership changes (CEO, team)│├── Technical Events│ ├── Upgrades (hard forks, updates)│ ├── Security incidents (hacks, exploits)│ ├── Network issues (congestion, downtime)│ └── Development milestones (testnet, audits)│├── Market Events│ ├── Whale movements (large transfers)│ ├── Exchange listings/delistings│ ├── Liquidations (large position closures)│ └── Market manipulation (pump/dump)│└── Macroeconomic Events ├── Interest rate decisions ├── Inflation data ├── Employment reports └── Geopolitical developmentsNews Data Sources
Primary Sources for Crypto Trading
┌────────────────────────────────────────────────────────────────────────┐│ News Data Sources │├────────────────────────────────────────────────────────────────────────┤│ ││ 1. Official Channels ││ ├── Project Twitter/X accounts ││ ├── Official blogs and announcements ││ ├── Discord/Telegram channels ││ └── GitHub releases and commits ││ ││ 2. News Aggregators ││ ├── CoinDesk, CoinTelegraph, The Block ││ ├── Decrypt, CryptoSlate ││ ├── Bloomberg Crypto, Reuters ││ └── RSS feeds aggregation ││ ││ 3. Social Media ││ ├── Twitter/X (crypto influencers, KOLs) ││ ├── Reddit (r/cryptocurrency, r/bitcoin) ││ ├── Telegram groups ││ └── Discord communities ││ ││ 4. On-Chain Data with Context ││ ├── Whale Alert with news correlation ││ ├── DEX activity + announcements ││ ├── Smart contract deployments ││ └── Governance proposals ││ ││ 5. Regulatory Filings ││ ├── SEC EDGAR (ETF applications) ││ ├── CFTC announcements ││ ├── International regulators ││ └── Court filings and decisions ││ │└────────────────────────────────────────────────────────────────────────┘Data Ingestion Pipeline
# Pseudocode for news data ingestionclass NewsDataPipeline: def __init__(self): self.sources = [ TwitterSource(accounts=CRYPTO_INFLUENCERS), RSSSource(feeds=NEWS_FEEDS), TelegramSource(channels=ANNOUNCEMENT_CHANNELS), WebScraperSource(sites=NEWS_SITES), ] self.deduplicator = SemanticDeduplicator() self.preprocessor = TextPreprocessor()
async def collect_news(self, timeframe: str) -> List[NewsItem]: all_news = [] for source in self.sources: news = await source.fetch(timeframe) all_news.extend(news)
# Remove duplicates based on semantic similarity unique_news = self.deduplicator.process(all_news)
# Preprocess text processed = [self.preprocessor.clean(n) for n in unique_news]
return processedLLM Architecture for News Interpretation
Model Selection for Trading
| Model Type | Use Case | Latency | Accuracy |
|---|---|---|---|
| GPT-4/Claude | Complex reasoning, multi-hop | High | Highest |
| GPT-3.5/Llama | Balance of speed and quality | Medium | High |
| FinBERT/FinGPT | Domain-specific, fast | Low | Good for sentiment |
| Custom fine-tuned | Specialized tasks | Low | Task-dependent |
Prompt Engineering for News Interpretation
┌─────────────────────────────────────────────────────────────────────────┐│ Prompt Template Structure │├─────────────────────────────────────────────────────────────────────────┤│ ││ SYSTEM PROMPT: ││ "You are a financial analyst specializing in cryptocurrency markets. ││ Your task is to analyze news and extract trading-relevant ││ information. Be precise, objective, and quantify uncertainty." ││ ││ USER PROMPT TEMPLATE: ││ """ ││ Analyze the following news for trading signals: ││ ││ NEWS: {news_text} ││ TIMESTAMP: {timestamp} ││ SOURCE: {source} ││ ││ Extract: ││ 1. Affected assets (list all cryptocurrencies mentioned) ││ 2. Event type (regulatory/corporate/technical/market/macro) ││ 3. Sentiment (-1 to +1 scale) ││ 4. Expected impact magnitude (low/medium/high) ││ 5. Timeframe of impact (immediate/short-term/long-term) ││ 6. Confidence level (0-100%) ││ 7. Key entities involved ││ 8. Potential market reactions ││ ││ Return as structured JSON. ││ """ ││ │└─────────────────────────────────────────────────────────────────────────┘Chain-of-Thought Reasoning for Complex News
┌─────────────────────────────────────────────────────────────────────────┐│ Chain-of-Thought Example │├─────────────────────────────────────────────────────────────────────────┤│ ││ News: "Binance announces withdrawal of services from Country X ││ due to new crypto regulations" ││ ││ Step 1: Identify entities ││ → Binance (exchange), Country X (jurisdiction) ││ ││ Step 2: Classify event ││ → Regulatory event (compliance-driven withdrawal) ││ ││ Step 3: Analyze immediate impact ││ → BNB: Potentially negative (reduced market access) ││ → Market: Short-term uncertainty ││ ││ Step 4: Consider second-order effects ││ → Competitors (Coinbase, Kraken) may gain market share ││ → Sets precedent for other jurisdictions ││ → May trigger sell pressure from Country X users ││ ││ Step 5: Assess long-term implications ││ → Regulatory clarity (positive for institutional adoption) ││ → Market maturation signal ││ ││ Final Signal: ││ BNB: -0.3 (short-term negative, medium confidence) ││ BTC: -0.1 (slight negative contagion, low confidence) ││ Exchange tokens (general): -0.2 (regulatory concerns) ││ │└─────────────────────────────────────────────────────────────────────────┘Feature Extraction Pipeline
Structured Output Schema
// Rust representation of extracted features#[derive(Debug, Clone, Serialize, Deserialize)]pub struct NewsAnalysis { /// Unique identifier for the news item pub news_id: String,
/// Original news text pub text: String,
/// Timestamp of the news pub timestamp: DateTime<Utc>,
/// Source of the news pub source: NewsSource,
/// Extracted entities pub entities: Vec<Entity>,
/// Event classification pub event_type: EventType,
/// Sentiment score (-1.0 to 1.0) pub sentiment: f64,
/// Expected impact magnitude pub magnitude: ImpactMagnitude,
/// Timeframe of expected impact pub timeframe: ImpactTimeframe,
/// Confidence in the analysis (0.0 to 1.0) pub confidence: f64,
/// Affected assets with individual signals pub asset_signals: HashMap<String, AssetSignal>,
/// Related news for context pub related_news_ids: Vec<String>,}
#[derive(Debug, Clone, Serialize, Deserialize)]pub struct AssetSignal { /// Asset symbol (e.g., "BTC", "ETH") pub symbol: String,
/// Direction signal (-1.0 to 1.0) pub direction: f64,
/// Confidence for this specific asset pub confidence: f64,
/// Expected price impact percentage pub expected_impact_pct: Option<f64>,
/// Duration of expected effect pub effect_duration: Duration,}Feature Aggregation Strategy
┌─────────────────────────────────────────────────────────────────────────┐│ News Signal Aggregation │├─────────────────────────────────────────────────────────────────────────┤│ ││ Multiple news items about the same asset within time window: ││ ││ News 1: BTC sentiment = +0.5, confidence = 0.8 ││ News 2: BTC sentiment = +0.3, confidence = 0.9 ││ News 3: BTC sentiment = -0.2, confidence = 0.6 ││ ││ Aggregation Methods: ││ ││ 1. Confidence-Weighted Average: ││ signal = Σ(sentiment_i × confidence_i) / Σ(confidence_i) ││ signal = (0.5×0.8 + 0.3×0.9 + (-0.2)×0.6) / (0.8+0.9+0.6) ││ signal = (0.4 + 0.27 - 0.12) / 2.3 = 0.24 ││ ││ 2. Recency-Weighted: ││ weight_i = exp(-λ × age_i) ││ More recent news has higher weight ││ ││ 3. Source-Quality Weighted: ││ Official sources > Major news > Social media ││ ││ 4. Event-Type Priority: ││ Regulatory > Security > Corporate > Market ││ ││ Combined Formula: ││ final_signal = Σ(s_i × c_i × r_i × q_i × p_i) / Σ(c_i × r_i × q_i × p_i)││ ││ Where: s=sentiment, c=confidence, r=recency, q=quality, p=priority ││ │└─────────────────────────────────────────────────────────────────────────┘Signal Generation Strategies
Strategy 1: Event-Driven Trading
┌─────────────────────────────────────────────────────────────────────────┐│ Event-Driven Signal Flow │├─────────────────────────────────────────────────────────────────────────┤│ ││ News Event → LLM Analysis → Event Classification → Trading Action ││ ││ Event Type Responses: ││ ││ REGULATORY_APPROVAL: ││ → Immediate long position ││ → Position size: 2× standard ││ → Stop loss: -3% ││ → Take profit: +10% or 24h ││ ││ SECURITY_INCIDENT: ││ → Immediate short position (if available) ││ → Or exit existing longs ││ → Monitor for recovery bounce ││ ││ PARTNERSHIP_ANNOUNCEMENT: ││ → Gradual entry over 1-4 hours ││ → Watch for "sell the news" pattern ││ → Use trailing stop ││ ││ WHALE_MOVEMENT: ││ → Wait for confirmation (price action) ││ → Align with direction of movement ││ → Tighter stops due to uncertainty ││ │└─────────────────────────────────────────────────────────────────────────┘Strategy 2: Sentiment Momentum
Sentiment Momentum Strategy:
1. Calculate rolling sentiment score: S_t = α × S_{t-1} + (1-α) × s_new
2. Generate signal when sentiment crosses thresholds: - S_t > +0.3 → Long signal - S_t < -0.3 → Short signal - |S_t| < 0.1 → Neutral/Close positions
3. Position sizing based on sentiment strength: size = base_size × |S_t| × confidence_factorStrategy 3: News Clustering and Theme Detection
┌─────────────────────────────────────────────────────────────────────────┐│ Theme-Based Trading │├─────────────────────────────────────────────────────────────────────────┤│ ││ Cluster news by semantic similarity: ││ ││ Cluster A: "Layer 2 adoption" Cluster B: "Regulatory" ││ ├── ARB mainnet milestone ├── SEC lawsuit update ││ ├── OP grants announcement ├── EU MiCA implementation ││ ├── zkSync TVL growth ├── Japan crypto tax reform ││ └── Base transaction record └── UAE regulatory framework ││ ││ Theme Strength = number of news × average sentiment × recency ││ ││ Trading Action: ││ - Strong positive theme in "Layer 2" → Long ARB, OP, MATIC ││ - Mixed theme in "Regulatory" → Hedge positions, reduce leverage ││ ││ Theme Rotation: ││ - Track theme momentum across time ││ - Rotate into strengthening themes ││ - Exit weakening themes ││ │└─────────────────────────────────────────────────────────────────────────┘Application to Cryptocurrency Trading
Bybit Integration for News-Based Trading
┌─────────────────────────────────────────────────────────────────────────┐│ Bybit News Trading Pipeline │├─────────────────────────────────────────────────────────────────────────┤│ ││ ┌───────────────────────────────────────────────────────────────────┐ ││ │ NEWS INGESTION LAYER │ ││ │ Twitter API ──→ ┐ │ ││ │ RSS Feeds ──→ │──→ Preprocessor ──→ Queue ──→ LLM Analyzer │ ││ │ Telegram ──→ │ │ ││ │ On-chain ──→ ┘ │ ││ └───────────────────────────────────────────────────────────────────┘ ││ │ ││ ↓ ││ ┌───────────────────────────────────────────────────────────────────┐ ││ │ SIGNAL GENERATION │ ││ │ │ ││ │ LLM Analysis ──→ Feature Extraction ──→ Signal Aggregation │ ││ │ │ │ ││ │ ↓ │ ││ │ Confidence Filter │ ││ │ (only signals > 0.7) │ ││ │ │ ││ └───────────────────────────────────────────────────────────────────┘ ││ │ ││ ↓ ││ ┌───────────────────────────────────────────────────────────────────┐ ││ │ EXECUTION LAYER │ ││ │ │ ││ │ Signal ──→ Risk Check ──→ Position Sizing ──→ Bybit API │ ││ │ │ │ ││ │ ↓ │ ││ │ ┌─────────────┐ │ ││ │ │ Order Types │ │ ││ │ ├─────────────┤ │ ││ │ │ • Limit │ │ ││ │ │ • Market │ │ ││ │ │ • TP/SL │ │ ││ │ └─────────────┘ │ ││ │ │ ││ └───────────────────────────────────────────────────────────────────┘ ││ │└─────────────────────────────────────────────────────────────────────────┘Real-Time Processing Requirements
| Component | Latency Target | Priority |
|---|---|---|
| News ingestion | < 1s | Critical |
| LLM inference | < 5s | High |
| Signal generation | < 100ms | Critical |
| Order execution | < 50ms | Critical |
| Risk checks | < 10ms | Critical |
Example: News Event Processing
Timeline of a news event:
T+0s: News posted on Twitter: "Breaking: ETH ETF approved by SEC"T+0.5s: Our system ingests the newsT+1.0s: Preprocessing completeT+3.0s: LLM analysis returns: { "asset": "ETH", "event_type": "REGULATORY_APPROVAL", "sentiment": 0.95, "magnitude": "HIGH", "confidence": 0.92 }T+3.1s: Signal generated: STRONG_BUY for ETHT+3.2s: Risk check passedT+3.3s: Position size calculated: 2% of portfolioT+3.4s: Order sent to Bybit: Market buy ETHUSDTT+3.5s: Order filled at $3,245T+3.6s: Stop-loss set at $3,147 (-3%)T+3.7s: Take-profit set at $3,570 (+10%)
Total time from news to position: 3.7 secondsImplementation Strategy
Module Architecture
68_llm_news_interpretation/├── Cargo.toml├── README.md├── README.ru.md├── readme.simple.md├── readme.simple.ru.md├── src/│ ├── lib.rs # Library root│ ├── news/│ │ ├── mod.rs # News module│ │ ├── sources.rs # Data source connectors│ │ ├── preprocessor.rs # Text preprocessing│ │ └── types.rs # News data types│ ├── llm/│ │ ├── mod.rs # LLM module│ │ ├── client.rs # API client (OpenAI, etc.)│ │ ├── prompts.rs # Prompt templates│ │ ├── parser.rs # Response parsing│ │ └── cache.rs # Response caching│ ├── analysis/│ │ ├── mod.rs # Analysis module│ │ ├── extractor.rs # Feature extraction│ │ ├── aggregator.rs # Signal aggregation│ │ └── classifier.rs # Event classification│ ├── strategy/│ │ ├── mod.rs # Strategy module│ │ ├── signals.rs # Signal generation│ │ └── execution.rs # Order execution│ ├── data/│ │ ├── mod.rs # Data module│ │ ├── bybit.rs # Bybit API client│ │ └── types.rs # Market data types│ └── utils/│ ├── mod.rs # Utilities│ ├── metrics.rs # Performance metrics│ └── config.rs # Configuration├── examples/│ ├── basic_news_analysis.rs # Basic LLM news analysis│ ├── backtest.rs # Backtesting with historical news│ └── live_trading.rs # Live trading demo└── tests/ └── integration.rs # Integration testsKey Design Principles
- Async-First: All I/O operations are asynchronous for low latency
- Modular LLM Support: Easy to swap between different LLM providers
- Caching Layer: Reduce redundant LLM calls for similar news
- Graceful Degradation: Fallback to simpler models if primary fails
- Audit Trail: Log all news, analyses, and trading decisions
Core Data Structures in Rust
/// News source enumeration#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]pub enum NewsSource { Twitter, Reddit, Telegram, CoinDesk, CoinTelegraph, TheBlock, Bloomberg, Reuters, OfficialBlog, GitHub, OnChain,}
/// Event type classification#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]pub enum EventType { RegulatoryApproval, RegulatoryEnforcement, RegulatoryLegislation, CorporatePartnership, CorporateAcquisition, ProductLaunch, LeadershipChange, TechnicalUpgrade, SecurityIncident, NetworkIssue, WhaleMovement, ExchangeListing, ExchangeDelisting, Liquidation, MacroEconomic, Unknown,}
/// Impact magnitude#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]pub enum ImpactMagnitude { Low, Medium, High, Critical,}
/// Impact timeframe#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]pub enum ImpactTimeframe { Immediate, // Minutes ShortTerm, // Hours MediumTerm, // Days LongTerm, // Weeks+}
/// Trading signal derived from news#[derive(Debug, Clone, Serialize, Deserialize)]pub struct TradingSignal { pub symbol: String, pub direction: SignalDirection, pub strength: f64, // 0.0 to 1.0 pub confidence: f64, // 0.0 to 1.0 pub source_news_ids: Vec<String>, pub event_type: EventType, pub suggested_entry: Option<f64>, pub suggested_stop_loss: Option<f64>, pub suggested_take_profit: Option<f64>, pub max_position_pct: f64, pub valid_until: DateTime<Utc>,}
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]pub enum SignalDirection { StrongBuy, Buy, Neutral, Sell, StrongSell,}Risk Management
News-Specific Risk Factors
┌────────────────────────────────────────────────────────────────────────┐│ News Trading Risk Management │├────────────────────────────────────────────────────────────────────────┤│ ││ 1. Information Risk ││ ├── Fake news / misinformation ││ ├── Delayed news (already priced in) ││ ├── Misinterpretation by LLM ││ └── Conflicting news sources ││ ││ Mitigation: ││ → Multi-source verification ││ → Confidence thresholds ││ → Source reputation scoring ││ → Human oversight for high-impact trades ││ ││ 2. Execution Risk ││ ├── Slippage during volatile news events ││ ├── Failed orders during high activity ││ ├── Latency disadvantage vs. HFT ││ └── Exchange downtime during major events ││ ││ Mitigation: ││ → Limit orders with reasonable slippage ││ → Multiple exchange redundancy ││ → Gradual position building ││ → Pre-positioned limit orders for expected events ││ ││ 3. Model Risk ││ ├── LLM hallucinations ││ ├── Prompt injection attacks ││ ├── Model updates changing behavior ││ └── Domain shift in news patterns ││ ││ Mitigation: ││ → Output validation and sanity checks ││ → Input sanitization ││ → Model versioning and testing ││ → Regular backtesting on recent data ││ │└────────────────────────────────────────────────────────────────────────┘Position Sizing Rules
Position Sizing Framework:
Base Position = Portfolio × Base_Pct × Confidence × Magnitude_Factor
Where:- Base_Pct = 1% (conservative base)- Confidence = LLM confidence score (0.0 to 1.0)- Magnitude_Factor: - Low: 0.5 - Medium: 1.0 - High: 1.5 - Critical: 2.0
Example:- Portfolio: $100,000- Confidence: 0.85- Magnitude: High (1.5)
Position = $100,000 × 0.01 × 0.85 × 1.5 = $1,275
Maximum Constraints:- Max single position: 5% of portfolio- Max news-based positions: 20% of portfolio- Max correlated positions: 10% of portfolioCircuit Breakers
- Rapid News Spike: Pause trading if >10 relevant news items in 5 minutes
- Sentiment Flip: Alert if sentiment changes >1.0 within 1 hour
- Confidence Drop: Reduce positions if average confidence drops below 0.5
- Loss Limit: Stop all news trading if daily loss exceeds 5%
Performance Metrics
Model Evaluation
| Metric | Description | Target |
|---|---|---|
| Sentiment Accuracy | Correlation with price movement | > 0.3 |
| Event Classification F1 | Correct event type identification | > 0.80 |
| Signal Precision | Profitable signals / Total signals | > 55% |
| Latency P95 | 95th percentile end-to-end latency | < 5s |
| False Positive Rate | Incorrect strong signals | < 10% |
Trading Performance
| Metric | Description | Target |
|---|---|---|
| Sharpe Ratio | Risk-adjusted returns | > 1.5 |
| Win Rate | Profitable trades | > 52% |
| Average Win/Loss | Ratio of avg win to avg loss | > 1.5 |
| Max Drawdown | Largest peak-to-trough | < 15% |
| News Alpha | Return attributable to news signals | > 0.5% monthly |
Latency Budget
┌─────────────────────────────────────────────────┐│ Latency Requirements │├─────────────────────────────────────────────────┤│ News Detection: < 500ms ││ Preprocessing: < 100ms ││ LLM Inference: < 3000ms ││ Signal Generation: < 100ms ││ Risk Check: < 50ms ││ Order Placement: < 100ms │├─────────────────────────────────────────────────┤│ Total Critical Path: < 4000ms │└─────────────────────────────────────────────────┘References
-
From Deep Learning to LLMs: A Survey of AI in Quantitative Investment
- URL: https://arxiv.org/abs/2503.21422
- Year: 2025
-
FinGPT: Open-Source Financial Large Language Models
- Yang, H., et al. (2023)
- URL: https://arxiv.org/abs/2306.06031
-
BloombergGPT: A Large Language Model for Finance
- Wu, S., et al. (2023)
- URL: https://arxiv.org/abs/2303.17564
-
Attention Is All You Need
- Vaswani, A., et al. (2017). NeurIPS
- Foundation of transformer architecture
-
Language Models are Few-Shot Learners (GPT-3)
- Brown, T., et al. (2020). NeurIPS
-
BERT: Pre-training of Deep Bidirectional Transformers
- Devlin, J., et al. (2019). NAACL
-
FinBERT: Financial Sentiment Analysis with Pre-trained Language Models
- Araci, D. (2019)
- URL: https://arxiv.org/abs/1908.10063
-
News-Driven Stock Price Forecasting
- Hu, Z., et al. (2018). IJCAI
-
Deep Learning for Event-Driven Stock Prediction
- Ding, X., et al. (2015). IJCAI
Next Steps
- View Simple Explanation - Beginner-friendly version
- Russian Version - Русская версия
- Run Examples - Working Rust code
Chapter 68 of Machine Learning for Trading