PyIndicators

🚀 The most user-friendly technical analysis library for Python! Combining Numba-powered performance with intelligent automation and beautiful visualizations.

100x easier to use than TA-Lib - with auto-detection, one-line analysis, built-in backtesting, streaming support, and interactive widgets!

✨ Key Features

• 🏎️ Blazing Fast: Numba JIT-compiled for C-like performance
• 🧠 Intelligent: Auto-detects data formats and column names
• 📊 Complete: 25+ indicators with built-in visualization and backtesting
• 🐼 Pandas Native: Full DataFrame/Series support with named outputs
• 📱 Interactive: Jupyter widgets and visual strategy builders
• ⚡ Real-time: Streaming indicators for live trading
• 🔧 Flexible: From one-liners to advanced pipelines
• 🛡️ Robust: Smart error handling with helpful suggestions

🚀 Installation

pip install pyindicators

# For interactive features
pip install pyindicators[widgets]

# For development
pip install pyindicators[dev]

⚡ Quick Start - Choose Your Style

🎯 Absolute Beginner (1 line!)

from pyindicators.easy import analyze

# Auto-detects columns, adds indicators, plots, and backtests!
results = analyze('your_data.csv')

🐼 Pandas User (Most Popular)

from pyindicators.easy import SmartDataFrame

# Works with any CSV/JSON/Parquet file or DataFrame
df = SmartDataFrame('AAPL.csv')  # Auto-detects OHLC columns!

# Add indicators with short names
df.add_indicators('rsi', 'macd', 'bb', 'volume')

# Or add everything at once
df.add_indicators('all')

# Built-in visualization with signals
df.plot()

# Instant backtesting
performance = df.backtest(strategy='macd_cross')
print(performance)

🔗 Pipeline Enthusiast (Advanced)

from pyindicators.pipeline import IndicatorPipeline

# Chain operations fluently
signals = (IndicatorPipeline(data)
    .rsi()
    .bollinger_bands()
    .macd()
    .add_signal('buy', lambda df: (df['RSI_14'] < 30) & (df['Close'] < df['BB_Lower']))
    .add_signal('sell', lambda df: (df['RSI_14'] > 70) & (df['Close'] > df['BB_Upper']))
    .golden_cross()
    .divergence()
    .get())

📊 Interactive User (Jupyter)

from pyindicators.widgets import interactive_analysis

# Launch interactive explorer with sliders and real-time updates
explorer = interactive_analysis('data.csv')

⚡ Live Trader (Real-time)

from pyindicators.streaming import LiveTrader

trader = LiveTrader()

# In your trading loop
result = trader.on_tick({
    'high': 105.2, 'low': 104.8, 
    'close': 105.0, 'volume': 1000000
})

if result['action'] == 'BUY':
    print(f"Buy signal at ${result['indicators']['close']}")

💻 Command Line User

# Quick analysis
pyindicators analyze AAPL.csv

# Calculate specific indicator
pyindicators calc data.csv -i rsi -p 21

# Run backtest
pyindicators backtest data.csv -s macd_cross -c 10000

# Find signals
pyindicators signals data.csv --action buy

🎛️ All Available Interfaces

Interface	Best For	Example
Easy Mode	Beginners, quick analysis	analyze('data.csv')
Pandas Wrapper	Data scientists	ta.rsi(df['Close'])
NumPy Core	Performance-critical	rsi(close_array, 14)
Pipelines	Complex strategies	Pipeline(data).rsi().macd()
Streaming	Live trading	trader.on_tick(live_data)
CLI	Terminal users	pyindicators analyze data.csv
Widgets	Jupyter notebooks	interactive_analysis(data)

📊 Available Indicators

🚀 Momentum (7 indicators)

• RSI (Relative Strength Index) - rsi()
• Stochastic Oscillator - stochastic()
• Williams %R - williams_r()
• Rate of Change - roc()
• Momentum - momentum()

📈 Trend (7 indicators)

• SMA (Simple Moving Average) - sma()
• EMA (Exponential Moving Average) - ema()
• WMA (Weighted Moving Average) - wma()
• DEMA (Double Exponential MA) - dema()
• TEMA (Triple Exponential MA) - tema()
• MACD (Moving Average Convergence Divergence) - macd()
• ADX (Average Directional Index) - adx()

📊 Volatility (5 indicators)

• Bollinger Bands - bollinger_bands()
• ATR (Average True Range) - atr()
• Keltner Channels - keltner_channels()
• Donchian Channels - donchian_channels()
• Standard Deviation - standard_deviation()

📦 Volume (5 indicators)

• OBV (On Balance Volume) - obv()
• VWAP (Volume Weighted Average Price) - vwap()
• A/D Line (Accumulation/Distribution) - ad()
• MFI (Money Flow Index) - mfi()
• CMF (Chaikin Money Flow) - cmf()

🎯 Built-in Trading Strategies

# Pre-built strategies ready to use
strategies = [
    'simple',       # RSI oversold/overbought
    'macd_cross',   # MACD crossover signals
    'bb_bounce',    # Bollinger Band mean reversion
    'trend_follow'  # Multi-indicator trend following
]

# Instant backtesting
results = df.backtest(strategy='macd_cross')

🎨 Advanced Features

🔍 Smart Data Detection

# Works with ANY column naming convention!
df = SmartDataFrame(data)  # Auto-detects: Close, close, CLOSE, Close_Price, etc.

🔗 Multi-Timeframe Analysis

from pyindicators.easy import MultiTimeframe

mtf = MultiTimeframe(data)
mtf.add_timeframe('daily', 'D')
mtf.add_timeframe('weekly', 'W')
confluence = mtf.find_confluence()  # Find signals across timeframes

🎯 Signal Detection

from pyindicators.easy import find_signals

# Find all trading signals automatically
signals = find_signals('AAPL.csv')
print(signals.head())

🛠️ Custom Indicators

# Create custom indicators easily
pipeline = (IndicatorPipeline(data)
    .custom(lambda df: df['Close'].rolling(20).skew(), 'Skewness_20')
    .transform('Volume', lambda x: np.log(x), 'Log_Volume'))

🎮 Interactive Visualization

from pyindicators.visual import launch_visual_tools

# Interactive chart with sliders
launch_visual_tools(data, tool='interactive')

# Strategy builder with drag-and-drop
launch_visual_tools(data, tool='builder')

# Indicator playground
launch_visual_tools(data, tool='playground')

🚨 Smart Error Handling

PyIndicators provides helpful error messages and suggestions:

# Instead of cryptic errors, you get:
❌ Column 'Close' not found in DataFrame!

Available columns: ['close', 'high', 'low', 'volume']

Did you mean one of these?
  • close

💡 Tip: Check your column names are correct. Common issues:
  - Case sensitivity (Close vs close)
  - Extra spaces in column names

🏎️ Performance

Still blazing fast with Numba optimization:

import time
from pyindicators import rsi

# 1 million data points
data = np.random.randn(1_000_000) + 100

start = time.time()
result = rsi(data, period=14)
elapsed = time.time() - start

print(f"Calculated RSI for 1M points in {elapsed:.3f} seconds")
# Output: Calculated RSI for 1M points in 0.045 seconds

📖 Complete Examples

📈 Full Technical Analysis Workflow

from pyindicators.easy import SmartDataFrame
import yfinance as yf

# Load data (or use any CSV/DataFrame)
df = yf.download('AAPL', start='2020-01-01')

# Create smart dataframe with auto-detection
sdf = SmartDataFrame(df)

# Add all indicators
sdf.add_indicators('all')

# Create custom strategy
sdf.add_signal('custom_buy', lambda df: 
    (df['RSI'] < 30) & 
    (df['MACD'] > df['MACD_Signal']) & 
    (df['Close'] < df['BB_Lower'])
)

# Backtest multiple strategies
strategies = ['simple', 'macd_cross', 'bb_bounce', 'trend_follow']
for strategy in strategies:
    results = sdf.backtest(strategy=strategy)
    print(f"{strategy}: {results['total_return']}")

# Beautiful visualization
sdf.plot(show_signals=True)

# Export results
sdf.df.to_csv('analysis_results.csv')

🔄 Streaming Analysis

from pyindicators.streaming import LiveTrader
import yfinance as yf

# Setup live trader
trader = LiveTrader()

# Simulate live data feed
historical_data = yf.download('AAPL', period='1mo', interval='1m')

for timestamp, row in historical_data.iterrows():
    tick_data = {
        'high': row['High'],
        'low': row['Low'], 
        'close': row['Close'],
        'volume': row['Volume']
    }
    
    result = trader.on_tick(tick_data)
    
    if result['action']:
        print(f"[{timestamp}] {result['action']} signal at ${tick_data['close']:.2f}")
        print(f"RSI: {result['indicators'].get('rsi', 'N/A'):.1f}")

# Get performance
performance = trader.get_performance()
print(f"Total PnL: {performance['total_pnl']}")

🎯 Strategy Comparison

from pyindicators.pipeline import StrategyPipeline

# Test multiple strategy setups
strategies = {
    'Conservative': StrategyPipeline(data).mean_reversion_setup(bb_period=20, rsi_period=21),
    'Aggressive': StrategyPipeline(data).trend_following_setup(fast=10, slow=30),
    'Breakout': StrategyPipeline(data).breakout_setup(period=15)
}

results = {}
for name, strategy in strategies.items():
    df_with_signals = strategy.get()
    # Calculate returns for each strategy
    # ... backtesting logic
    results[name] = performance_metrics

print("Strategy Performance Comparison:")
print(pd.DataFrame(results).T)

📚 Documentation & Tutorials

• Getting Started Guide - Complete beginner tutorial
• API Reference - Full function documentation
• Strategy Guide - Trading strategy examples
• Performance Tips - Optimization techniques
• Jupyter Examples - Interactive notebook tutorials

🧪 Development

Running Tests

# Install development dependencies
pip install -e .[dev]

# Run all tests
pytest

# Run with coverage
pytest --cov=pyindicators --cov-report=html

# Run specific test file
pytest tests/test_pandas_wrapper.py -v

Benchmarking

# Performance benchmarks
python examples/benchmark.py

# Memory usage analysis
python examples/memory_profile.py

Code Quality

# Format code
black pyindicators tests

# Lint code  
ruff check pyindicators tests

# Type checking
mypy pyindicators

🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Add tests for your changes
4. Ensure all tests pass (pytest)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

💡 Ideas for Contributions

• New technical indicators
• Additional trading strategies
• Performance optimizations
• Documentation improvements
• Integration with data providers (Alpha Vantage, Quandl, etc.)
• Additional visualization options

📄 License

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

🙏 Acknowledgments

• Numba Team - For the incredible JIT compiler
• Pandas Team - For the amazing DataFrame functionality
• TA-Lib - For inspiration and reference implementations
• Technical Analysis Community - For indicator formulas and expertise

💬 Support & Community

• 🐛 Bug Reports: GitHub Issues
• 💡 Feature Requests: GitHub Discussions
• 📧 Email: [email protected]
• 💬 Discord: Join our community

⭐ Star History

If you find PyIndicators helpful, please give it a ⭐️ on GitHub! It helps others discover the project.

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Made with ❤️ for the trading and data science community

"The best technical analysis library you'll ever use!" - Happy Users