pyita 1.0.12

Technical analysis library for stock quotes

pyita

pyita is a simple and fast technical analysis library for Python, written in pure Python with NumPy and Numba for maximum performance. It is ideal for Python-based financial market analysis, algorithmic trading, and working with data from any source.

Features

• Pure native Python - written in clean Python, easy to read, understand, and modify
• Blazingly fast - uses NumPy vectorized operations and Numba JIT compilation for maximum performance
• Simple API - intuitive interface for working with OHLCV data
• Rich set of indicators - 28 popular technical indicators
• Flexible - easy to create custom indicators
• Independent - no external data sources required, bring your own data from any source
• Compatible - supports Python 3.9+ (tested up to 3.14)
• Integrations - works with NumPy arrays, pandas DataFrames, and CCXT

Installation

From PyPI:

pip install pyita

From source:

git clone https://github.com/hal9000cc/pyita.git
cd pyita
pip install -e .

Quick Start

From NumPy arrays:

import pyita as ta
import numpy as np

# Create quotes from arrays
quotes = ta.Quotes(
    open=np.array([100.0, 102.0, 101.0, 103.0]),
    high=np.array([105.0, 106.0, 104.0, 107.0]),
    low=np.array([99.0, 101.0, 100.0, 102.0]),
    close=np.array([102.0, 103.0, 101.0, 105.0])
)

# Calculate indicator
sma = ta.sma(quotes, period=3, value='close')
print(sma.sma)  # [nan, nan, 102.0, 103.0]

From pandas DataFrame:

import pandas as pd
import pyita as ta

# Load data from CSV
df = pd.read_csv('data.csv')
quotes = ta.Quotes(df)

# Calculate Bollinger Bands
bb = ta.bollinger_bands(quotes, period=20, deviation=2)
print(bb.up_line)    # Upper band
print(bb.mid_line)   # Middle line
print(bb.down_line)  # Lower band

From CCXT (cryptocurrency exchanges):

import ccxt
import pyita as ta

# Fetch data from exchange
exchange = ccxt.binance()
ohlcv = exchange.fetch_ohlcv('BTC/USDT', '1h', limit=100)
quotes = ta.Quotes(ohlcv)

# Calculate RSI
rsi = ta.rsi(quotes, period=14)
print(rsi.rsi)  # Values from 0 to 100

Creating Quotes Object

The Quotes object encapsulates OHLCV data and can be created from various sources.

Minimal variant (OHLC only):

import numpy as np
import pyita as ta

quotes = ta.Quotes(
    open=np.array([100.0, 102.0, 101.0]),
    high=np.array([105.0, 106.0, 104.0]),
    low=np.array([99.0, 101.0, 100.0]),
    close=np.array([102.0, 103.0, 101.0])
)

With volume:

quotes = ta.Quotes(
    open=open_prices,
    high=high_prices,
    low=low_prices,
    close=close_prices,
    volume=np.array([1000, 1200, 900])
)

With volume and time:

quotes = ta.Quotes(
    open=open_prices,
    high=high_prices,
    low=low_prices,
    close=close_prices,
    volume=volume,
    time=np.array(['2024-01-01', '2024-01-02', '2024-01-03'], dtype='datetime64[ms]')
)

From pandas DataFrame:

import pandas as pd

df = pd.DataFrame({
    'open': [100.0, 102.0],
    'high': [105.0, 106.0],
    'low': [99.0, 101.0],
    'close': [102.0, 103.0],
    'volume': [1000, 1200]
})

quotes = ta.Quotes(df)

From dictionary:

data = {
    'open': [100.0, 102.0],
    'high': [105.0, 106.0],
    'low': [99.0, 101.0],
    'close': [102.0, 103.0]
}

quotes = ta.Quotes(data)

From list of lists (CCXT format):

# CCXT returns: [[timestamp, open, high, low, close, volume], ...]
ohlcv = [
    [1609459200000, 100.0, 105.0, 99.0, 102.0, 1000],
    [1609545600000, 102.0, 106.0, 101.0, 103.0, 1200]
]

quotes = ta.Quotes(ohlcv)

Available Indicators

Moving Averages

• sma(quotes, period, value='close') - Simple Moving Average

  • Requires: OHLC
  • Returns: sma

• ema(quotes, period, value='close') - Exponential Moving Average

  • Requires: OHLC
  • Returns: ema

• tema(quotes, period, value='close') - Triple Exponential Moving Average

  • Requires: OHLC
  • Returns: tema

• vwma(quotes, period, value='close') - Volume Weighted Moving Average

  • Requires: OHLC + volume
  • Returns: vwma

• ma(quotes, period, value='close', ma_type='sma') - Moving Average (universal)

  • Requires: OHLC
  • Returns: ma
  • See Moving Average Types for available ma_type options

Trend Indicators

• adx(quotes, period=14, smooth=14, ma_type='mma') - Average Directional Index

  • Requires: OHLC
  • Returns: adx, p_di (Plus DI), m_di (Minus DI)

• aroon(quotes, period=14) - Aroon Indicator

  • Requires: OHLC (uses high, low)
  • Returns: up, down, oscillator

• parabolic_sar(quotes, start=0.02, maximum=0.2, increment=0.02) - Parabolic SAR

  • Requires: OHLC
  • Returns: sar, signal

• supertrend(quotes, period=10, multiplier=3, ma_type='mma') - SuperTrend

  • Requires: OHLC
  • Returns: supertrend, signal

• macd(quotes, period_fast=12, period_slow=26, period_signal=9, value='close') - Moving Average Convergence Divergence

  • Requires: OHLC
  • Returns: macd, signal, histogram

• ichimoku(quotes, period_short=9, period_mid=26, period_long=52, offset_senkou=26, offset_chikou=26) - Ichimoku Cloud

  • Requires: OHLC (uses high, low)
  • Returns: tenkan, kijun, senkou_a, senkou_b, chikou

Oscillators

• rsi(quotes, period=14, ma_type='mma', value='close') - Relative Strength Index

  • Requires: OHLC
  • Returns: rsi (values from 0 to 100)

• stochastic(quotes, period=5, period_d=3, smooth=3, ma_type='sma') - Stochastic Oscillator

  • Requires: OHLC
  • Returns: value_k, value_d, oscillator

• williams_r(quotes, period=14) - Williams %R

  • Requires: OHLC
  • Returns: williams_r (values from -100 to 0)

• cci(quotes, period=20) - Commodity Channel Index

  • Requires: OHLC
  • Returns: cci

• mfi(quotes, period=14) - Money Flow Index

  • Requires: OHLC + volume
  • Returns: mfi

• roc(quotes, period=14, value='close') - Rate of Change

  • Requires: OHLC
  • Returns: roc

• awesome(quotes, period_fast=5, period_slow=34, normalized=False) - Awesome Oscillator

  • Requires: OHLC (uses high, low)
  • Returns: awesome

• trix(quotes, period, value='close') - Triple Exponential Average Oscillator

  • Requires: OHLC
  • Returns: trix

Volatility

• bollinger_bands(quotes, period=20, deviation=2, ma_type='sma', value='close') - Bollinger Bands

  • Requires: OHLC
  • Returns: mid_line, up_line, down_line, width, z_score

• atr(quotes, smooth=14, ma_type='mma') - Average True Range

  • Requires: OHLC
  • Returns: atr, atrp (percentage ATR), tr (True Range)

• keltner(quotes, period=10, multiplier=1, period_atr=10, ma_type='ema') - Keltner Channels

  • Requires: OHLC
  • Returns: mid_line, up_line, down_line, width

• chandelier(quotes, period=22, multiplier=3, use_close=False) - Chandelier Exit

  • Requires: OHLC
  • Returns: exit_long, exit_short

Volume Indicators

• obv(quotes) - On-Balance Volume

  • Requires: OHLC + volume (uses close, volume)
  • Returns: obv

• vwap(quotes) - Volume Weighted Average Price

  • Requires: OHLC + volume
  • Returns: vwap

• volume_osc(quotes, period_short=5, period_long=10, ma_type='ema') - Volume Oscillator

  • Requires: volume
  • Returns: osc (percentage difference between short and long MA of volume)

• adl(quotes, ma_period=None, ma_type='sma') - Accumulation/Distribution Line

  • Requires: OHLC + volume
  • Returns: adl, adl_ema (if ma_period is specified)

Other Indicators

• zigzag(quotes, delta=0.02, depth=1, type='high_low', end_points=False) - ZigZag
  • Requires: OHLC
  • Returns: pivots, pivot_types (1 = High, -1 = Low, 0 = no pivot)

Moving Average Types

Many indicators support selecting the moving average type via the ma_type parameter:

• sma - Simple Moving Average

  • All values have equal weight
  • Initialization: SMA over first period values

• ema - Exponential Moving Average

  • More weight given to recent values
  • Smoothing coefficient: α = 2 / (period + 1)
  • Initialization: SMA over first period values

• mma (or smma, rma) - Modified/Smoothed Moving Average

  • Similar to EMA but with slower response
  • Smoothing coefficient: α = 1 / period
  • Initialization: SMA over first period values
  • Used in RSI, ADX indicators

• ema0 - EMA with first value initialization

  • No warmup period required

• mma0 - MMA with first value initialization

  • No warmup period required

• emaw - EMA with dynamic warmup period (TA-Lib compatible)

• mmaw - MMA with dynamic warmup period (TA-Lib compatible)

Essentially, technical analysis uses only two moving average algorithms - simple (sma) and exponential (ema). SMA is always calculated the same way. EMA is also calculated consistently, but there can be variations in how it uses the period (smoothing coefficient calculation) and how it's initialized. This is what distinguishes the different ema types supported by pyita.

Usage example:

# Bollinger Bands with EMA
bb = ta.bollinger_bands(quotes, period=20, deviation=2, ma_type='ema')

# Keltner Channels with MMA
keltner = ta.keltner(quotes, period=20, multiplier=2, period_atr=10, ma_type='mma')

# RSI uses MMA by default (can be changed)
rsi = ta.rsi(quotes, period=14, ma_type='ema')

System Requirements

• Python: 3.9+ (tested up to 3.14)
• Required dependencies:
  • numpy >= 1.20.0
  • numba >= 0.53.0
• Optional dependencies:
  • pandas >= 1.3.0 (for DataFrame support)

Testing

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

# Run tests
pytest tests/

License

MIT License

Copyright (c) 2026 Aleksandr Kuznetsov [e-mail]

Contact

• GitHub Issues: https://github.com/hal9000cc/pyita/issues
• Email: [e-mail]