v1indicators 0.2.0

Technical analysis indicators and scanners.

v1indicators

v1indicators is a professional-grade, high-performance technical analysis library for Python. It is designed to be the "Standard Library" for financial indicators—prioritizing mathematical accuracy, system stability, and zero external bloat.

It is a pure calculation engine. It does not trade, it does not plot, and it does not make promises. It just does the math.

The Philosophy

Existing libraries (ta-lib, pandas-ta, ta) often suffer from one of three problems: dependency hell (C-compilers), abandoned maintenance, or bloated "black box" object hierarchies.

v1indicators solves this by adhering to four rules:

1. Math > Magic: Implementations are based on standard textbook formulas (Wilder, Lane, Bollinger).
2. Simple > Fancy: A pure functional API. Input arrays, output arrays. No complex classes.
3. Hybrid JIT Engine: Recursive indicators (Supertrend, PSAR) are compiled to machine code using Numba (@jit) for C-level speed, while vectorizable logic uses optimized NumPy.
4. Reliability: 100% test coverage with strict type validation and fault-tolerance (no silent crashes on empty data).

Installation

Requires Python 3.9+, NumPy, Pandas, and Numba.

pip install .

Quick Start

The API is standardized. All functions accept Pandas Series and return Pandas Series (for single-value indicators) or DataFrames (for multi-value indicators).

import pandas as pd
from v1indicators import rsi, macd, supertrend

# 1. Load your data (Engineering Standard: Lowercase columns)
df = pd.read_csv("data.csv")  # Must have 'open', 'high', 'low', 'close', 'volume'

# 2. Simple Indicator (RSI)
# Returns a Series named 'RSI_14'. Handles Zero-Loss (infinite gain) cases safely.
df['RSI'] = rsi(df['close'], length=14)

# 3. Complex Indicator (MACD)
# Returns a DataFrame with 'MACD', 'MACD_SIGNAL', 'MACD_HIST'
macd_df = macd(df['close'], fast=12, slow=26, signal=9)
df = pd.concat([df, macd_df], axis=1)

# 4. Pro Indicator (Supertrend)
# Uses Numba JIT Kernel for high-speed recursive calculation
# Returns 'SUPERTREND' and 'SUPERTREND_DIR'
st_df = supertrend(df['high'], df['low'], df['close'], length=10, mult=3.0)
df = pd.concat([df, st_df], axis=1)

print(df.tail())

Available Indicators

We currently support the "Essential 20"—the indicators used by 90% of professional systematic traders.

Momentum

• RSI (Relative Strength Index) - Zero-Division Safe
• MACD (Moving Average Convergence Divergence)
• Stochastic (Stochastic Oscillator)
• ROC (Rate of Change)
• MFI (Money Flow Index)
• CCI (Commodity Channel Index)

Overlap (Trend Filters)

• SMA (Simple Moving Average)
• EMA (Exponential Moving Average)
• WMA (Weighted Moving Average)
• RMA (Wilder's Smoothing / Running Moving Average)
• Bollinger Bands
• Keltner Channels
• Donchian Channels
• Ichimoku Cloud

Trend (JIT Optimized)

• Supertrend (Numba Accelerated)
• Parabolic SAR (PSAR) (Numba Accelerated)
• ADX (Average Directional Index)

Volatility

• ATR (Average True Range)

Volume

• OBV (On-Balance Volume)
• VWAP (Volume Weighted Average Price)

Levels

• Fibonacci Retracements

Development

We enforce strict engineering standards.

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

# Run the test suite (100% Pass Rate Required)
pytest

License

MIT