Crypto Pattern Backtest Datasets
Free download of our complete backtest results for 30 crypto chart patterns. Raw data from Binance historical OHLCV, 2018–2026. No registration required.
All Patterns — CSV 120 rows · 12 columns · 30 patterns × 4 assets
Download CSV Summary — JSON 30 patterns · Metadata + success rates
Download JSON Dataset Overview
Patterns Covered
30
Candlestick, chart, and indicator patterns
Assets
4
BTC, ETH, SOL, BNB (USDT pairs)
Data Range
2018–2026
Binance historical OHLCV
Total Occurrences
16,000+
Pattern instances across all assets
Timeframes
2
Daily (1D) and 4-Hour (4H)
License
CC BY 4.0
Free to use with attribution
CSV Column Definitions
| Column | Type | Description | Example |
|---|---|---|---|
pattern_slug | string | URL-safe pattern identifier | hammer-candlestick |
pattern_name | string | Human-readable pattern name | Hammer Candlestick |
asset | string | Trading pair on Binance | BTC/USDT |
occurrences | integer | Number of pattern instances detected | 287 |
success_rate_pct | float | % of instances that hit take profit | 63.4 |
failure_rate_pct | float | % of instances that hit stop loss | 36.6 |
avg_gain_pct | float | Average gain on successful trades (%) | 5.2 |
avg_loss_pct | float | Average loss on failed trades (%, negative) | -2.3 |
rr_ratio | string | Risk/reward ratio | 2.3:1 |
data_range | string | Historical data period | 2018-2026 |
exchange | string | Data source exchange | Binance |
last_updated | date | Date of last backtest recalculation | 2026-06-14 |
Complete Backtest Results — All 30 Patterns
30 patterns
| Pattern | Success Rate ▾ | Occurrences | Assets | Updated | Full Report |
|---|---|---|---|---|---|
| Ascending Triangle | 68.9% | 278 | SOL/USDT, ETH/USDT, BTC/USDT, BNB/USDT | 2026-06-14 | View → |
| Bear Flag | 65.2% | 589 | SOL/USDT, BTC/USDT, BNB/USDT, ETH/USDT | 2026-06-14 | View → |
| Bearish Engulfing | 61.8% | 892 | SOL/USDT, BTC/USDT, ETH/USDT, BNB/USDT | 2026-06-14 | View → |
| Bollinger Band Squeeze | 61.7% | 445 | BTC/USDT, SOL/USDT, ETH/USDT, BNB/USDT | 2026-06-14 | View → |
| Bull Flag | 67.8% | 623 | SOL/USDT, BNB/USDT, ETH/USDT, BTC/USDT | 2026-06-14 | View → |
| Bullish Engulfing | 64.1% | 847 | BNB/USDT, BTC/USDT, SOL/USDT, ETH/USDT | 2026-06-14 | View → |
| Cup and Handle | 74.1% | 156 | BTC/USDT, SOL/USDT, BNB/USDT, ETH/USDT | 2026-06-14 | View → |
| Dark Cloud Cover | 62.1% | 534 | BTC/USDT, SOL/USDT, ETH/USDT, BNB/USDT | 2026-06-14 | View → |
| Death Cross | 76.2% | 84 | BTC/USDT, ETH/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Descending Triangle | 67.4% | 264 | ETH/USDT, BTC/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Doji Candlestick | 53.2% | 1,340 | BTC/USDT, ETH/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Double Bottom | 69.4% | 342 | ETH/USDT, BTC/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Double Top | 68.2% | 412 | BNB/USDT, ETH/USDT, BTC/USDT, SOL/USDT | 2026-06-14 | View → |
| Evening Star | 66.7% | 298 | BTC/USDT, SOL/USDT, ETH/USDT, BNB/USDT | 2026-06-14 | View → |
| Golden Cross | 78.4% | 89 | ETH/USDT, BTC/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Hammer Candlestick | 62.4% | 1,024 | ETH/USDT, BNB/USDT, SOL/USDT, BTC/USDT | 2026-06-14 | View → |
| Hanging Man | 57.3% | 689 | BTC/USDT, SOL/USDT, ETH/USDT, BNB/USDT | 2026-06-14 | View → |
| Head and Shoulders | 71.2% | 187 | BTC/USDT, ETH/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Inverse Head and Shoulders | 72.8% | 194 | BTC/USDT, SOL/USDT, ETH/USDT, BNB/USDT | 2026-06-14 | View → |
| Inverted Hammer | 57.9% | 712 | ETH/USDT, BTC/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| MACD Bearish Crossover | 58.6% | 912 | SOL/USDT, BTC/USDT, ETH/USDT, BNB/USDT | 2026-06-14 | View → |
| MACD Bullish Crossover | 59.8% | 934 | BNB/USDT, BTC/USDT, SOL/USDT, ETH/USDT | 2026-06-14 | View → |
| Morning Star | 68.3% | 312 | SOL/USDT, BNB/USDT, ETH/USDT, BTC/USDT | 2026-06-14 | View → |
| Piercing Line | 63.4% | 478 | BTC/USDT, ETH/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Rising Wedge | 69.8% | 198 | ETH/USDT, SOL/USDT, BTC/USDT, BNB/USDT | 2026-06-14 | View → |
| RSI Bearish Divergence | 63.2% | 678 | ETH/USDT, SOL/USDT, BTC/USDT, BNB/USDT | 2026-06-14 | View → |
| RSI Bullish Divergence | 64.8% | 847 | BNB/USDT, BTC/USDT, ETH/USDT, SOL/USDT | 2026-06-14 | View → |
| Shooting Star | 60.4% | 756 | ETH/USDT, BTC/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
| Symmetrical Triangle | 61.2% | 312 | ETH/USDT, SOL/USDT, BTC/USDT, BNB/USDT | 2026-06-14 | View → |
| Volume Spike | 56.3% | 1,124 | ETH/USDT, BTC/USDT, SOL/USDT, BNB/USDT | 2026-06-14 | View → |
How to Use This Dataset
import pandas as pd
# Load the dataset
df = pd.read_csv('youpattern-backtest-results.csv')
# Top 5 patterns by success rate (minimum 200 occurrences)
top5 = (df[df['occurrences'] >= 200]
.groupby('pattern_name')['success_rate_pct']
.mean()
.sort_values(ascending=False)
.head(5))
print(top5)
# Filter BTC-only results
btc = df[df['asset'] == 'BTC/USDT']
print(btc[['pattern_name', 'success_rate_pct', 'occurrences']].sort_values('success_rate_pct', ascending=False))library(readr)
library(dplyr)
df <- read_csv("youpattern-backtest-results.csv")
# Top patterns by success rate
df %>%
filter(occurrences >= 200) %>%
group_by(pattern_name) %>%
summarise(avg_success = mean(success_rate_pct),
total_occ = sum(occurrences)) %>%
arrange(desc(avg_success)) %>%
head(10)- Download the CSV file above
- Open Excel or Google Sheets
- File → Import → Upload CSV
- Use Data → AutoFilter to filter by asset or success rate
- Insert a Pivot Table to compare patterns across assets
Data Methodology & Limitations
Data source: Binance public API, historical OHLCV data for BTC/USDT, ETH/USDT, SOL/USDT, and BNB/USDT from January 2018 to June 2026.
Pattern detection: Algorithmic detection using the exact criteria documented on each pattern's backtest page. No manual curation.
Success definition: A pattern is counted as successful if the price reaches the take-profit level (typically 1× ATR above entry) before hitting the stop-loss level (below pattern low/high) within a 30-candle holding window.
Important limitations: All success rates are gross of trading fees and slippage. Real-world results will be lower. Past performance on historical data does not guarantee future results. This dataset is provided for research and educational purposes only.
How the Average Success Rate Is Calculated
The dataset contains two aggregate metrics that appear in different contexts:
| Metric | Value | Formula | When to use |
|---|---|---|---|
| Simple average | 65.1% | Sum of all 30 pattern success rates ÷ 30 | Comparing patterns equally regardless of sample size |
| Weighted average | 62.1% | Sum of (success_rate × occurrences) ÷ total occurrences (16,550) | Reflecting actual expected performance across all observed instances |
The simple average (65.1%) is used in headline summaries. The weighted average (62.1%) is the more statistically accurate figure and is used in the Dataset schema. The difference arises because high-frequency patterns (e.g., Doji, Hammer) have more occurrences and tend to have lower success rates than rare patterns.
Read the full methodology → · Editorial policy → · Data sources →
How to Cite This Dataset
YouPattern Research Team. (2026). Crypto Chart Pattern Backtest Results Dataset (Version 1.0) [Data set]. YouPattern. https://youpattern.com/backtests/datasets/ (CC BY 4.0)