Automate technical analysis. Detect 20+ chart patterns algorithmically with confidence scores, eliminate human bias, and discover which patterns actually work in your market.
Chart pattern recognition is the automated detection of technical price formations that historically signal potential trading opportunities with measurable probability edges.
For decades, traders have manually drawn trendlines and identified patterns like triangles, flags, and head-and-shoulders formations. The problem? Human pattern recognition is subjective, inconsistent, and prone to confirmation bias. You see what you want to see.
Algorithmic pattern recognition eliminates these problems. Advanced algorithms scan price data mathematically, identifying patterns based on precise geometric criteria - swing high/low relationships, trendline angles, support/resistance levels, and symmetry measurements.
Each detected pattern receives a confidence score (0-100%) measuring how closely it matches the textbook definition. A 95% confidence ascending triangle means it's a near-perfect match. A 55% confidence pattern is borderline and likely unreliable.
The real power: You can backtest which patterns actually work in your market. Maybe bull flags perform great on daily SPY but fail on 15-minute crypto. Algorithmic detection lets you quantify pattern success rates objectively instead of trading on hope and textbook rules.
Humans see patterns everywhere (even in random data). Algorithms detect patterns based on mathematical criteria only, removing confirmation bias and emotional influence.
Scan years of data across multiple symbols in seconds. Every pattern is evaluated with identical criteria - no morning vs evening fatigue affecting pattern quality.
Backtest pattern success rates objectively. Discover that head-and-shoulders works 68% of the time in your market, while cup-and-handle only works 42%. Trade the winners.
Every pattern gets a quality rating. Filter for 70%+ confidence patterns to trade only high-quality setups and avoid ambiguous formations.
The pattern detection engine uses mathematical models to identify formations:
Algorithm scans price data to identify significant swing highs and lows - local peaks and troughs that represent key turning points. These form the anchor points for pattern geometry.
Connect swing points to form potential trendlines (support, resistance, converging lines). Measure angles, lengths, and touchpoints. For triangles, check if upper and lower trendlines converge properly.
Compare identified formations against mathematical templates for each pattern type. For example, ascending triangle requires: flat resistance (horizontal top), rising support (upward bottom), 2+ touches on each line, and apex convergence.
Score how well the formation matches the ideal pattern (0-100%). Factors: trendline quality (R² value), symmetry, number of touchpoints, volume patterns, and deviation from ideal ratios. Higher scores = better pattern quality.
Provide pattern details: type, confidence score, start/end dates, breakout level, measured target, risk/reward ratio, volume confirmation, and reliability rating. This metadata enables filtering and strategy building.
| Aspect | Manual Detection | Algorithmic Detection |
|---|---|---|
| Consistency | Varies by trader mood, fatigue | Identical criteria every time |
| Bias | Confirmation bias, wishful thinking | Objective mathematical criteria |
| Speed | Minutes per chart | Thousands of patterns per second |
| Backtesting | Subjective, time-consuming | Automated, comprehensive |
| Quality Measurement | Subjective judgment | Precise confidence scores |
| Scalability | Limited to few symbols | Scan entire markets |
| Best For | Discretionary trading | Systematic trading, validation |
BacktestMe detects 20+ chart patterns across four categories:
How far back to search for pattern formations:
Don't detect all patterns - be selective based on your strategy:
Additional filters to reduce false positives:
Excellent pattern quality. Near-perfect match to textbook definition. Clean trendlines, proper symmetry, correct ratios. These patterns have the highest success rates and are safe to trade with standard risk management.
Example: Ascending triangle with 92% confidence - perfectly flat resistance, rising support with 4 touchpoints, ideal apex convergence.
Good pattern quality. Solid match with minor imperfections. Trendlines mostly clean, symmetry acceptable. Recommended threshold for most trading. Still reliable with proper risk management.
Example: Bull flag with 76% confidence - clear uptrend, rectangular consolidation with slight irregularity, overall structure intact.
Marginal pattern quality. Recognizable pattern but with notable imperfections. Trendlines loose, asymmetry issues, or missing touchpoints. Require additional confirmation signals (volume, indicators) before trading.
Example: Double top with 64% confidence - two peaks at similar level but not perfectly aligned, neckline has slight slope.
Poor pattern quality. Do not trade. Pattern barely matches definition. Likely false positive or random price noise. High failure rate. Filter these out completely in your strategy settings.
Example: Head and shoulders with 48% confidence - three peaks but wrong proportions, shoulders uneven, neckline erratic.
Backtested 37 bull flag occurrences with 70%+ confidence over 3 years:
Bull flags on AAPL daily charts show statistically significant edge. Pattern is tradable with proper risk management.
Filter for high-quality patterns only. Don't trade patterns below 70% confidence - they have significantly higher failure rates. Quality over quantity always wins in pattern trading.
Trade continuation patterns (flags, pennants) only in direction of the larger trend. Don't trade bull flags in downtrends. This single filter improves win rates by 15-20%.
Breakouts on high volume (1.5-2x average) are significantly more reliable than low-volume breakouts. Volume confirms genuine buying/selling pressure vs false breakouts.
Not all patterns work equally in all markets. Test which patterns have the highest success rates in your specific instrument and timeframe. Trade the winners, ignore the losers.
If trading daily patterns, check that the weekly chart supports the move. Higher timeframe alignment dramatically improves reliability. Don't fight the higher timeframe trend.
Use measured moves (pattern height projected from breakout) but don't be greedy. Taking partial profits at 60-70% of target increases overall profitability by reducing winners that reverse into losers.
Patterns below 60% confidence are essentially random noise. They fail at least 60-70% of the time. Stick to 70%+ confidence and save yourself the losses. Low-quality patterns aren't worth the risk, even if they're frequent.
A perfect 95% confidence bull flag means nothing in a strong downtrend. Always check higher timeframe trend and overall market conditions. Context determines whether patterns work or fail catastrophically.
Entering after price has already moved 5-10% past breakout point eliminates your edge and destroys risk/reward ratio. Enter on breakout or first pullback, not after the move is already done.
Every pattern has a invalidation level (e.g., below support for bull patterns). If price violates this level, the pattern failed - exit immediately. Don't hope and hold. Failed patterns often lead to significant losses.
Some patterns work great in certain markets and fail in others. Head-and-shoulders might be 75% reliable on stocks but only 45% on crypto. Always backtest pattern performance in your specific market before trading.
Algorithmic pattern detection uses mathematical models to identify specific price formations. The algorithm analyzes swing highs/lows, trendlines, support/resistance levels, and geometric relationships to match patterns. Each detected pattern receives a confidence score (0-100%) based on how closely it matches the ideal pattern shape. The algorithm measures trendline quality, symmetry, touchpoints, and volume to determine confidence.
A confidence score measures how well a detected pattern matches the textbook definition. 90%+ means near-perfect match with clean trendlines and proper proportions. 70-89% is good quality suitable for trading. 60-69% is acceptable but requires additional confirmation. Below 60% is low quality with high failure rates. Higher confidence patterns typically have better success rates in live trading.
Reliability varies by market and timeframe, but consistently strong patterns include: Bull/Bear flags (continuation patterns with 65-75% success rates), Ascending/Descending triangles (directional breakouts, 60-70% reliable), Double top/bottom (reversal patterns, 55-65%), and Cup and handle (continuation, 60-70%). Always backtest pattern performance in your specific market - what works on stocks may fail on forex or crypto.
No. Chart patterns work best when combined with confirmation signals. Use trend filters (trade with the larger trend), volume confirmation (breakouts on high volume), momentum indicators (RSI direction), and multiple timeframe alignment. For example, a bull flag with RSI > 50, 2x average volume on breakout, and weekly uptrend has 15-20% higher success rate than the pattern alone.
Higher timeframes (4H, daily) produce more reliable patterns with better risk/reward ratios but fewer signals. Lower timeframes (5min, 15min) offer more opportunities but higher false positives. Most swing traders use daily or 4-hour charts for best balance. Day traders use 15min-1H. Position traders use weekly. Test your timeframe preference - reliability increases with timeframe.
Use multiple filters: (1) High confidence thresholds (70%+), (2) Volume confirmation on breakouts, (3) Trade with the trend not against it, (4) Multiple timeframe alignment, (5) Avoid low-volatility periods where patterns are less meaningful, (6) Backtest to identify which patterns actually work in your market. Most false patterns are eliminated by requiring 70%+ confidence plus one confirmation signal.