Blog
Data-driven insights on chart patterns, market structure, and how visual pattern search works.
How to Backtest Stock Chart Patterns: A Step-by-Step Guide Using Real Data
Learn how to backtest chart patterns against 24 million historical embeddings. Step-by-step guide using Chart Library's API to test bull flags, breakouts, and more with real forward return data.
Bull Flag Success Rate: Updated Data From 24 Million Chart Patterns
Updated 2026 analysis of bull flag success rates across 24 million chart patterns. Real win rates by timeframe, what separates winners from losers, and how bull flags compare to random entries.
How AI Agents Analyze Stocks: A Complete Guide to Chart Library's MCP Tools
Learn how AI agents like Claude and ChatGPT use MCP tools to analyze stock charts. Set up Chart Library's MCP server in 5 minutes and give your AI access to 24 million historical chart patterns.
What NVDA Typically Does After Earnings (10 Years of Data)
A data-driven look at how NVIDIA stock has behaved after quarterly earnings reports. Base rates, average 1/5/10-day moves, and the pattern intelligence behind NVDA's post-earnings drift.
NVDA Gap Up History: Follow-Through and Fade Rates
Historical data on what happens after NVDA gaps up at the open. Win rates, average continuation, and when to fade versus chase.
NVDA Breakout Pattern: Success Rate and Average Return
How often does NVDA break out above a 20-day high and actually follow through? Historical win rates, average forward returns, and the role of volume confirmation.
NVDA Historical Volatility: What the Numbers Actually Mean
NVIDIA's typical daily range, largest single-day moves, realized volatility by year, and what it tells you about position sizing.
How NVDA Moves on Fed Days: A Decade of Data
NVIDIA's historical reaction to FOMC decisions — average moves, win rates, and how NVDA compares to SPY on rate announcement days.
What TSLA Typically Does After Earnings (10 Years of Data)
A data-driven look at how Tesla stock behaves after quarterly earnings. Win rates, average 1/5/10-day moves, and the wide distribution of post-earnings reactions.
TSLA Gap Up History: Data on Continuation vs Fade
How often Tesla gap ups follow through versus fade. Win rates by gap size, intraday patterns, and 5-day forward returns.
TSLA Breakout Pattern: Success Rate and Average Return
How often Tesla breaks out above a 20-day high and follows through. Historical win rates, average forward returns, and the effect of volume.
TSLA Historical Volatility: The Real Numbers
Tesla's realized volatility by year, typical daily range, largest historical moves, and how volatility regimes affect forward returns.
How TSLA Moves on Fed Days: A Decade of Data
Tesla's historical reaction to FOMC meetings. Win rates, average moves, and the dovish vs hawkish asymmetry for one of the most rate-sensitive mega-caps.
What AAPL Typically Does After Earnings (10 Years of Data)
Apple's post-earnings behavior across a decade. Base rates, average 1/5/10-day moves, and why AAPL reactions have become more muted over time.
AAPL Gap Up History: Follow-Through Data
How Apple stock behaves after gapping up. Win rates, intraday fade probability, and 5-day forward returns across a decade.
AAPL Breakout Pattern: Success Rate and Average Return
Apple's historical breakout follow-through data. Win rates on 20-day highs, the volume filter effect, and comparisons to other mega-caps.
AAPL Historical Volatility: What the Numbers Say
Apple's realized volatility, daily range statistics, tail event frequency, and how volatility regimes affect forward returns.
How AAPL Moves on Fed Days: A Decade of Data
Apple's historical FOMC reactions. Win rates, average moves, dovish vs hawkish asymmetry, and why AAPL is less rate-sensitive than people think.
What AMD Typically Does After Earnings (10 Years of Data)
AMD's post-earnings behavior. Base rates, average 1/5/10-day moves, and why AMD earnings reactions have grown more volatile as the company has scaled.
AMD Gap Up History: Follow-Through Data
How AMD behaves after gapping up. Win rates by gap size, intraday fade probability, and 5-day forward returns.
AMD Breakout Pattern: Success Rate and Average Return
AMD's historical breakout data. 20-day high follow-through rates, volume effect, and comparison to other semis.
AMD Historical Volatility: What the Numbers Say
AMD's realized volatility, daily range, tail events, and how volatility regimes affect forward returns.
How AMD Moves on Fed Days: A Decade of Data
AMD's historical FOMC reactions. Win rates, dovish vs hawkish asymmetry, and why semis are among the most rate-sensitive groups.
What SPY Does Around Earnings Season: A Decade of Data
SPY's behavior during earnings season. Average moves during peak weeks, base rates for earnings-season rallies, and why SPY reactions are muted but systematic.
SPY Gap Up History: Follow-Through and Fade Data
How the S&P 500 ETF behaves after gapping up. Base rates, fade probability, and what the gap-direction signal tells you about the broader market.
SPY Breakout Pattern: Success Rate and Average Return
How often SPY breaks out to 20-day highs and what happens next. Historical base rates, volume effects, and the role of market regime.
SPY Historical Volatility: The Baseline for Everything
SPY's realized volatility over a decade, typical daily range, biggest moves, and why SPY is the baseline for all other volatility comparisons.
How SPY Moves on Fed Days: A Decade of Data
SPY's historical behavior on FOMC days. Win rates, average moves, dovish vs hawkish asymmetry, and the notorious 2:30pm reversal pattern.
Pattern Similarity Search vs Traditional Technical Indicators: What's the Difference?
How does vector similarity search compare to rule-based technical indicators like RSI, MACD, and named patterns? We break down the approaches, trade-offs, and when to use each.
How to Use an MCP Server for Stock Analysis with Claude
Install Chart Library's MCP server and give Claude Desktop access to 24 million historical chart patterns, market regime data, and forward return statistics — all through natural conversation.
What Is a Market Regime Tracker? How Chart Library Identifies Historical Market Parallels
Learn what market regimes are, how Chart Library's regime tracker finds historically similar market conditions, and how to use regime data to inform your trading decisions.
Build a Stock Research Agent with LangChain + Chart Library in 20 Minutes
A hands-on tutorial for building an AI agent that can answer natural-language questions about stock charts, market regimes, and sector rotation using LangChain and Chart Library's pattern intelligence API.
Multi-Agent Stock Research with CrewAI + Chart Library
Build a multi-agent research crew that combines chart pattern analysis with market regime intelligence using CrewAI and Chart Library's API. Two specialist agents collaborate to produce institutional-quality research briefings.
AI Stock Chart Pattern Recognition: How Vector Similarity Finds Historical Analogs
How Chart Library uses mathematical vector embeddings and similarity search to match stock chart patterns against 10 years of historical data — and why it works better than traditional pattern detection.
Double Bottom Pattern: What the Data Shows
We analyzed our database of 16 million chart patterns to measure real double bottom performance. Success rates, average returns, and what separates reliable double bottoms from traps.
Cup and Handle Pattern: Historical Success Rate
What does the data actually say about cup and handle patterns? We measured real success rates, average returns, and optimal entry timing across 16 million historical charts.
How to Read Stock Chart Patterns: A Data-Driven Guide
Learn to read stock chart patterns with data, not dogma. A comprehensive guide to the most common patterns, what the data says about each one, and how to use historical precedents instead of guesswork.
Ascending Triangle Pattern: Does It Actually Work?
We tested the ascending triangle pattern against 16 million historical charts. Real breakout success rates, failure rates, and the volume signals that actually predict outcomes.
What Happens After a Stock Breaks Out? Data from 16 Million Charts
We analyzed breakout patterns across 16 million historical charts to answer the question every trader asks: what actually happens after a stock breaks out? Success rates, follow-through data, and what separates real breakouts from traps.
How to Find Similar Chart Patterns in Seconds
A step-by-step guide to using Chart Library's visual search engine to find historically similar stock chart patterns and see what happened next.
Do Chart Patterns Actually Predict Returns? What the Data Says
We analyzed millions of historical chart patterns to answer the age-old question: do chart patterns have predictive power? Here's what the data shows.
Bull Flag Pattern: What 16 Million Charts Tell Us
We analyzed millions of chart embeddings to measure real bull flag performance. Average returns, win rates, and how to spot the setups that actually work.
Stock Breakout Patterns in 2025: Lessons from the Data
What worked and what didn't in stock breakout patterns during 2025. Data-driven analysis of breakout success rates, failed breakouts, and key takeaways.
How Chart Library's Screenshot Search Actually Works
A technical explainer on how Chart Library converts a stock chart screenshot into a searchable embedding and finds matching patterns across millions of historical charts.
Head and Shoulders Pattern: Does It Actually Work?
We measured the real performance of head and shoulders patterns across thousands of stocks. Win rates, average returns, and what separates reliable signals from noise.
Stock Chart Analysis: A Data-Driven Guide for Beginners
Learn the fundamentals of stock chart analysis with a modern, data-driven approach. No guesswork — just patterns, statistics, and what the historical record actually shows.
Chart Pattern Scanners Compared: How Chart Library Is Different
A comparison of chart pattern scanning tools — TradingView, TrendSpider, Finviz — and how Chart Library's embedding-based approach differs from rule-based pattern detection.