Price Action Library

A comprehensive Python library for pure price action analysis, specifically optimized for the Indian stock market. Generate 95+ columns of price action analysis with a single function call!

🚀 Key Features

• 30+ Candlestick Patterns - From basic doji to complex triple formations
• 15+ Chart Patterns - Head & shoulders, triangles, wedges, flags, and more
• Advanced Market Structure - BOS, ChoCh, Fair Value Gaps, Order Blocks
• Support & Resistance - Multiple detection methods with confluence analysis
• Volume Analysis - Volume Spread Analysis (VSA) and volume patterns
• Price Action Setups - Pin bars, inside bars, fakey patterns, springs
• Gap Analysis - All gap types with classification and tracking
• Session Analysis - Indian market hours optimization (9:15 AM - 3:30 PM IST)
• Multi-Timeframe - Analyze across different time horizons
• Machine Learning Ready - Structured output perfect for ML models

🎯 Perfect For

• Systematic Trading - Algorithm development and backtesting
• Market Research - Comprehensive price action studies
• Machine Learning - Feature engineering for ML models
• Professional Trading - Real-time pattern recognition
• Education - Learning price action concepts

⚡ Quick Start

Installation

# Install dependencies
pip install pandas numpy scipy

# Install the library
pip install .

Basic Usage

from price_action_lib import PriceActionAnalyzer
import pandas as pd

# Initialize analyzer
analyzer = PriceActionAnalyzer()

# Load your OHLCV data (1-minute timeframe recommended)
# df should have columns: open, high, low, close, volume with DateTime index

# Get comprehensive analysis - 95+ columns!
result = analyzer.fetch_all(df)

print(f"Generated {result.shape[1]} columns of analysis!")
print(f"Analyzed {result.shape[0]} time periods")

# Individual analysis methods also available
patterns = analyzer.detect_candlestick_patterns(df)
structure = analyzer.analyze_market_structure(df)
levels = analyzer.find_support_resistance(df)

Sample Output

The fetch_all() method returns a DataFrame with 95+ columns including:

• Original OHLCV (optional)
• 44+ Candlestick patterns (pattern_doji, pattern_hammer, etc.)
• 14+ Chart patterns (chart_head_and_shoulders, chart_triangle, etc.)
• Market structure (structure_trend, structure_bos, etc.)
• Fair Value Gaps (fvg_bullish, fvg_bearish)
• Order Blocks (order_block_bullish, order_block_bearish)
• Price Action Setups (setup_pin_bar, setup_inside_bar, etc.)
• Support/Resistance (near_support, near_resistance)
• Volume Analysis (volume_spike, volume_climax, etc.)
• Gap Analysis (gap_breakaway, gap_exhaustion, etc.)
• Metadata (ATR, volatility, trend classification, price zones)

📊 Example Analysis

# Find high-probability trading setups
strong_signals = result[
    (result['near_support'] == True) &      # At key support level
    (result['setup_pin_bar'] == True) &     # Pin bar setup
    (result['volume_spike'] == True) &      # Volume confirmation
    (result['pattern_hammer'] != '')        # Hammer pattern
]

print(f"Found {len(strong_signals)} high-probability setups!")

# Analyze market structure
current_trend = result['structure_trend'].iloc[-1]
trend_strength = result['structure_trend_strength'].iloc[-1]
print(f"Current trend: {current_trend} (strength: {trend_strength:.2f})")

🇮🇳 Indian Market Optimization

• Market Hours: 9:15 AM - 3:30 PM IST
• Session Analysis: Pre-open, opening, regular, closing sessions
• Holiday Handling: Automatic weekend and holiday filtering
• NSE/BSE Compatible: Optimized for Indian stock exchanges

📈 Performance

• Fast Processing: 400+ bars/second on typical hardware
• Memory Efficient: Only 308KB package size
• Vectorized Operations: Optimized pandas/numpy operations
• Scalable: Handles datasets from 100 to 10,000+ bars

📚 Comprehensive Documentation

For detailed documentation, see DOCUMENTATION.md which includes:

• Complete API Reference - All methods with parameters and examples
• Pattern Recognition Guide - Details on all 30+ candlestick patterns
• Market Structure Analysis - BOS, ChoCh, FVGs, Order Blocks explained
• Advanced Features - Multi-timeframe, volume analysis, gap analysis
• Best Practices - Data preparation, performance optimization
• Troubleshooting - Common issues and solutions
• Real-world Examples - Complete trading workflows

🔧 Requirements

• Python: 3.7 or higher
• pandas: >= 1.3.0
• numpy: >= 1.21.0
• scipy: >= 1.7.0

📋 Data Format

Your DataFrame should have:

                     open    high     low   close  volume
2024-01-15 09:15:00  1000.0  1002.5   999.0  1001.5  25000
2024-01-15 09:16:00  1001.5  1003.0  1000.5  1002.0  18000
# ... with DateTime index and proper OHLC relationships

🚦 Quick Test

Verify your installation:

from price_action_lib import PriceActionAnalyzer
analyzer = PriceActionAnalyzer()
print("✅ Price Action Library installed successfully!")

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎉 Ready to Get Started?

1. Install the library following the instructions above
2. Read the comprehensive DOCUMENTATION.md
3. Load your OHLCV data into a pandas DataFrame
4. Run analyzer.fetch_all(df) and get 95+ columns of analysis!

Happy Trading! 📈💰