quantjourney-ti 0.2.1

Comprehensive technical indicators for financial data.

QuantJourney Technical Indicators

A high-performance Python library for calculating technical indicators, optimized with Numba for speed and designed for financial data analysis. This project is part of the Quantitative Infrastructure initiative by QuantJourney, providing robust tools for traders and researchers.

License: MIT License - see LICENSE.md for details.
Author: Jakub Polec ([e-mail])
Repository: github.com/QuantJourneyOrg/qj_technical_indicators

Overview

The QuantJourney Technical Indicators library offers a comprehensive set of technical indicators for analyzing financial time series data. Key features include:

• Numba-Optimized Calculations: Fast, JIT-compiled functions for performance-critical computations.
• Flexible API: Supports both standalone functions and a TechnicalIndicators class for object-oriented usage.
• Robust Error Handling: Validates inputs and handles edge cases like NaNs and empty data.
• Visualization: Generates individual plots for indicators, saved as PNG files in an indicator_plots directory.
• Integration: Works seamlessly with pandas DataFrames and yfinance for data fetching.

The library is ideal for backtesting trading strategies, real-time analysis, and research, with a focus on simplicity and extensibility.

Project Structure

The repository is organized as follows:

quantjourney_ti/
├── __init__.py               # Package initialization and imports
├── _decorators.py            # Decorators for timing and fallback mechanisms
├── _errors.py                # Custom error classes for input validation
├── _indicator_kernels.py     # Numba-optimized functions for indicator calculations
├── _legacy_/                 # Legacy code (not actively maintained)
├── _utils.py                 # Utility functions for validation, plotting, and memory optimization
├── indicators.py             # Main API class (TechnicalIndicators) with public methods
├── docs/                     # Documentation
│   ├── INDICATORS.md         # Explanation of each indicator
├── examples/                 # Example scripts demonstrating usage
│   ├── example_basic.py      # Basic indicator calculations
│   ├── example_indicators.py # Advanced usage with multiple indicators and plotting
├── tests/                    # Unit and integration tests
│   ├── __init__.py
│   ├── _yf.py                # yfinance test utilities
│   ├── test_all_indicators.py # Tests for all indicators
│   ├── test_basic.py         # Basic functionality tests
│   ├── test_decorators.py    # Decorator tests
│   ├── test_demo.py          # Demo script tests
│   ├── test_indicators.py    # Individual indicator tests
│   ├── test_integration_yf.py # Integration tests with yfinance
│   ├── test_utils.py         # Utility function tests
├── quantjourney_ti.egg-info/ # Package metadata (generated, typically in .gitignore)
├── README.md                 # Project documentation (this file)
├── LICENSE.md                # License details
├── setup.py                  # Package installation configuration

Note: The quantjourney_ti.egg-info directory is generated during package installation (e.g., pip install -e .). It can be safely removed if not using editable mode, and should be included in .gitignore to avoid version control.

Installation

1. Clone the repository:

   git clone https://github.com/QuantJourneyOrg/qj_technical_indicators.git
   cd qj_technical_indicators
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install the package in editable mode:

   pip install -e .
   

Requirements:

• Python 3.8+
• pandas, numpy, yfinance, numba, matplotlib

Usage

The library provides a TechnicalIndicators class for calculating indicators and saving plots. The examples/run_indicators.py script fetches PL data, calculates 20 popular indicators, and saves individual plots to an indicator_plots directory. Example:

from quantjourney_ti import TechnicalIndicators
from quantjourney_ti._utils import plot_indicators
import pandas as pd
import yfinance as yf
import os

# Fetch data
df = yf.download("PL", start="2024-01-01", end="2025-02-01")
if isinstance(df.columns, pd.MultiIndex):
    df.columns = df.columns.get_level_values(0).str.lower().str.replace(' ', '_')
else:
    df.columns = df.columns.str.lower().str.replace(' ', '_')
df["volume"] = df["volume"].replace(0, np.nan).ffill()

# Calculate indicators
ti = TechnicalIndicators()
indicators = [
    ("SMA", lambda: ti.SMA(df["close"], period=14)),
    ("EMA", lambda: ti.EMA(df["close"], period=14)),
    # ... 18 more indicators
]
results = {name: func() for name, func in indicators}

# Save plots
os.makedirs("indicator_plots", exist_ok=True)
for name, result in results.items():
    plot_indicators_dict = {name: result if isinstance(result, pd.Series) else result.iloc[:, 0]}
    plot_indicators(df, plot_indicators_dict, title=f"{name} Indicator", save_path=f"indicator_plots/{name}_plot.png")

Run the example:

python examples/run_indicators.py

Output:

• Console: Last 5 and final values for each indicator.
• Files: PNG plots in indicator_plots/ (e.g., SMA_plot.png).

📊 Example Plot

Supported Indicators

The library supports 39 indicators (54 series):

• Single-Series Indicators (21):
  • SMA (Simple Moving Average)
  • EMA (Exponential Moving Average)
  • RSI (Relative Strength Index)
  • ATR (Average True Range)
  • MFI (Money Flow Index)
  • TRIX
  • CCI (Commodity Channel Index)
  • ROC (Rate of Change)
  • WILLR (Williams %R)
  • DEMA (Double Exponential Moving Average)
  • KAMA (Kaufman Adaptive Moving Average)
  • AO (Awesome Oscillator)
  • ULTIMATE_OSCILLATOR
  • CMO (Chande Momentum Oscillator)
  • DPO (Detrended Price Oscillator)
  • MASS_INDEX
  • VWAP (Volume Weighted Average Price)
  • AD (Accumulation/Distribution Line)
  • HULL_MA (Hull Moving Average)
  • OBV (On-Balance Volume)
  • RVI (Relative Vigor Index)
• Multi-Series Indicators (18):
  • MACD (MACD, Signal, Histogram)
  • BB (Bollinger Bands: BB_Upper, BB_Middle, BB_Lower)
  • STOCH (Stochastic Oscillator: K, D)
  • ADX (Average Directional Index: ADX, +DI, -DI)
  • ICHIMOKU (Tenkan-sen, Kijun-sen, Senkou Span A, Senkou Span B, Chikou Span)
  • KELTNER (Keltner Channels: KC_Upper, KC_Middle, KC_Lower)
  • DONCHIAN (Donchian Channels: DC_Upper, DC_Middle, DC_Lower)
  • AROON (AROON_UP, AROON_DOWN, AROON_OSC)
  • VOLUME_INDICATORS (Volume_SMA, Force_Index, VPT)
  • PIVOT_POINTS (PP, R1, R2, S1, S2)
  • RAINBOW (9 SMAs for periods 2-10)
  • BETA
  • DI (Directional Indicator: +DI, -DI)
  • ADOSC (Chaikin A/D Oscillator)
  • HEIKEN_ASHI (HA_Open, HA_High, HA_Low, HA_Close)
  • BENFORD_LAW (Observed, Expected)
  • MOMENTUM_INDEX (MomentumIndex, NegativeIndex)
  • ELDER_RAY (BullPower, BearPower)

See indicators.py for the full list and parameters.

Development

To contribute:

1. Fork the repository and create a branch.
2. Add new indicators in _indicator_kernels.py with Numba optimization.
3. Define public methods in indicators.py.
4. Update tests in tests/.
5. Submit a pull request.

Testing:

pytest tests/

Cleaning: Remove generated files:

rm -rf quantjourney_ti.egg-info dist build

Future Work

• Add more indicators (e.g., PPO, Ichimoku Cloud).
• Enhance plotting with customizable layouts.
• Optimize Numba functions for additional edge cases.
• Support real-time data feeds.

Contact

For issues or feedback, contact Jakub Polec at [e-mail] or open an issue on GitHub.