Python API Reference

TechKit Python bindings provide high-performance technical analysis with NumPy integration.

Installation

pip install techkit

Requirements:

Python 3.10+
NumPy >= 1.21.0 (installed automatically)

Quick Reference

Object-Oriented API

from techkit import SMA, EMA, RSI, MACD, BBANDS

# Create indicator
sma = SMA(period=20)

# Incremental update
result = sma.update(price)
if result.valid:
    print(result.value)

# Batch calculation
import numpy as np
prices = np.array([...])
values = sma.calculate(prices)

TA-Lib Compatible API

from techkit import talib_compat as ta

# Same signatures as TA-Lib
sma = ta.SMA(prices, timeperiod=20)
rsi = ta.RSI(prices, timeperiod=14)
macd, signal, hist = ta.MACD(prices)
upper, middle, lower = ta.BBANDS(prices)

Module: `techkit`

Result Classes

IndicatorResult

@dataclass
class IndicatorResult:
    value: float    # Calculated value
    valid: bool     # True if valid, False during warmup
    
    def __bool__(self) -> bool:
        return self.valid

Usage:

result = sma.update(100.0)
if result:  # Uses __bool__
    print(f"SMA: {result.value}")

MACDResult

@dataclass
class MACDResult:
    macd: float       # MACD line
    signal: float     # Signal line
    histogram: float  # Histogram
    valid: bool
    
    def __iter__(self):
        return iter([self.macd, self.signal, self.histogram])

Usage:

result = macd.update(price)
if result.valid:
    m, s, h = result  # Tuple unpacking via __iter__

BBandsResult

@dataclass
class BBandsResult:
    upper: float
    middle: float
    lower: float
    valid: bool

StochResult

@dataclass  
class StochResult:
    k: float
    d: float
    valid: bool

Base Indicator Class

class Indicator:
    def update(self, value: float) -> IndicatorResult:
        """Update with single value (close price)."""
        
    def update_ohlcv(self, open: float, high: float, low: float, 
                     close: float, volume: float) -> IndicatorResult:
        """Update with OHLCV bar."""
        
    def calculate(self, data: np.ndarray) -> np.ndarray:
        """Batch calculate. Returns NaN for warmup period."""
        
    def reset(self) -> None:
        """Reset indicator state."""
        
    @property
    def lookback(self) -> int:
        """Number of bars before first valid output."""
        
    @property
    def name(self) -> str:
        """Indicator name."""
        
    @property
    def is_ready(self) -> bool:
        """True if indicator has produced valid output."""

Indicator Classes

Moving Averages

Class	Constructor	Input	Description
`SMA`	`SMA(period=20)`	close	Simple Moving Average
`EMA`	`EMA(period=20)`	close	Exponential Moving Average
`WMA`	`WMA(period=20)`	close	Weighted Moving Average
`DEMA`	`DEMA(period=20)`	close	Double EMA
`TEMA`	`TEMA(period=20)`	close	Triple EMA
`KAMA`	`KAMA(period=30)`	close	Kaufman Adaptive MA
`TRIMA`	`TRIMA(period=30)`	close	Triangular MA
`T3`	`T3(period=5, vfactor=0.7)`	close	T3 Moving Average

Momentum Indicators

Class	Constructor	Input	Description
`RSI`	`RSI(period=14)`	close	Relative Strength Index
`MACD`	`MACD(fast=12, slow=26, signal=9)`	close	MACD (3 outputs)
`STOCH`	`STOCH(k=14, k_slow=3, d=3)`	OHLCV	Stochastic (2 outputs)
`ADX`	`ADX(period=14)`	OHLCV	Average Directional Index
`CCI`	`CCI(period=20)`	OHLCV	Commodity Channel Index
`WILLR`	`WILLR(period=14)`	OHLCV	Williams %R
`MFI`	`MFI(period=14)`	OHLCV	Money Flow Index
`MOM`	`MOM(period=10)`	close	Momentum
`ROC`	`ROC(period=10)`	close	Rate of Change
`APO`	`APO(fast=12, slow=26)`	close	Absolute Price Oscillator
`PPO`	`PPO(fast=12, slow=26)`	close	Percentage Price Oscillator
`CMO`	`CMO(period=14)`	close	Chande Momentum Oscillator
`TRIX`	`TRIX(period=30)`	close	Triple Exponential Average
`AROON`	`AROON(period=14)`	OHLCV	Aroon Indicator (2 outputs)
`ULTOSC`	`ULTOSC(p1=7, p2=14, p3=28)`	OHLCV	Ultimate Oscillator

Volatility Indicators

Class	Constructor	Input	Description
`ATR`	`ATR(period=14)`	OHLCV	Average True Range
`NATR`	`NATR(period=14)`	OHLCV	Normalized ATR
`TRANGE`	`TRANGE()`	OHLCV	True Range
`BBANDS`	`BBANDS(period=20, std_up=2.0, std_dn=2.0)`	close	Bollinger Bands (3 outputs)

Volume Indicators

Class	Constructor	Input	Description
`OBV`	`OBV()`	OHLCV	On Balance Volume
`AD`	`AD()`	OHLCV	Accumulation/Distribution
`ADOSC`	`ADOSC(fast=3, slow=10)`	OHLCV	A/D Oscillator

Statistics Functions

Class	Constructor	Input	Description
`STDDEV`	`STDDEV(period=5, nbdev=1.0)`	close	Standard Deviation
`VAR`	`VAR(period=5)`	close	Variance
`LINEARREG`	`LINEARREG(period=14)`	close	Linear Regression
`LINEARREG_SLOPE`	`LINEARREG_SLOPE(period=14)`	close	Regression Slope
`LINEARREG_INTERCEPT`	`LINEARREG_INTERCEPT(period=14)`	close	Regression Intercept
`LINEARREG_ANGLE`	`LINEARREG_ANGLE(period=14)`	close	Regression Angle
`TSF`	`TSF(period=14)`	close	Time Series Forecast
`BETA`	`BETA(period=5)`	close + close	Beta Coefficient
`CORREL`	`CORREL(period=30)`	close + close	Pearson Correlation
`MIN`	`MIN(period=30)`	close	Minimum Value
`MAX`	`MAX(period=30)`	close	Maximum Value
`SUM`	`SUM(period=30)`	close	Sum over Period
`MINMAX`	`MINMAX(period=30)`	close	Min and Max (2 outputs)

Risk Metrics

Class	Constructor	Input	Description
`SharpeRatio`	`SharpeRatio(period=252, rfr=0.0, annualization=252)`	returns	Rolling Sharpe Ratio
`SortinoRatio`	`SortinoRatio(period=252, rfr=0.0, target=0.0, annualization=252)`	returns	Sortino Ratio
`MaxDrawdown`	`MaxDrawdown()`	price	Maximum Drawdown
`Drawdown`	`Drawdown()`	price	Drawdown Series
`CalmarRatio`	`CalmarRatio(period=756, annualization=252)`	returns	Calmar Ratio
`HistoricalVaR`	`HistoricalVaR(period=252, confidence=0.95)`	returns	Historical VaR
`CVaR`	`CVaR(period=252, confidence=0.95)`	returns	Conditional VaR

Volatility Models

Class	Constructor	Input	Description
`EWMAVolatility`	`EWMAVolatility(lambda=0.94, annualization=252)`	returns	EWMA Volatility
`RealizedVolatility`	`RealizedVolatility(period=20, annualization=252)`	returns	Realized Volatility
`ParkinsonVolatility`	`ParkinsonVolatility(period=20, annualization=252)`	OHLCV	Parkinson Volatility
`GARCHVolatility`	`GARCHVolatility(omega=0.000001, alpha=0.09, beta=0.90, annualization=252)`	returns	GARCH(1,1) Volatility

Pattern Recognition

Class	Constructor	Input	Description
`HarmonicPattern`	`HarmonicPattern(deviation_pct=5.0, tolerance=0.03, max_bars=100)`	OHLCV	Harmonic Patterns
`ChartPattern`	`ChartPattern(min_bars=20, max_bars=200, tolerance=0.03)`	OHLCV	Chart Patterns
`ZigZag`	`ZigZag(deviation_pct=5.0, depth=10)`	OHLCV	ZigZag Indicator
`SwingHighLow`	`SwingHighLow(left_bars=5, right_bars=5)`	OHLCV	Swing Detection
`PivotPoints`	`PivotPoints(type='classic')`	OHLCV	Pivot Points

Candlestick Patterns (61 patterns)

All CDL classes follow the same interface:

from techkit import CDL_DOJI, CDL_HAMMER, CDL_ENGULFING

doji = CDL_DOJI()
result = doji.calculate_ohlcv(open, high, low, close, volume)
# result: array of signals (+100 bullish, -100 bearish, 0 none)

Chain Class

class Chain:
    """Chain multiple indicators together.
    
    Output of first indicator feeds into second, etc.
    Useful for smoothing or combining indicators.
    
    Example:
        >>> # Smoothed RSI: RSI(14) -> EMA(9)
        >>> chain = Chain([RSI(14), EMA(9)])
        >>> result = chain.update(price)
        
        >>> # Batch
        >>> smoothed = chain.calculate(prices)
    """
    
    def __init__(self, indicators: List[Indicator]): ...
    def update(self, value: float) -> IndicatorResult: ...
    def calculate(self, data: np.ndarray) -> np.ndarray: ...
    def reset(self) -> None: ...

Module: `techkit.talib_compat`

Drop-in replacement for TA-Lib. Same function signatures.

Migration from TA-Lib

# Before (TA-Lib)
import talib
sma = talib.SMA(prices, timeperiod=20)
macd, signal, hist = talib.MACD(prices)

# After (TechKit)
from techkit import talib_compat as ta
sma = ta.SMA(prices, timeperiod=20)  # Same!
macd, signal, hist = ta.MACD(prices)  # Same!

Function Reference

Overlap Studies

Function	Signature	Returns
`SMA`	`SMA(real, timeperiod=30)`	np.ndarray
`EMA`	`EMA(real, timeperiod=30)`	np.ndarray
`WMA`	`WMA(real, timeperiod=30)`	np.ndarray
`DEMA`	`DEMA(real, timeperiod=30)`	np.ndarray
`TEMA`	`TEMA(real, timeperiod=30)`	np.ndarray
`KAMA`	`KAMA(real, timeperiod=30)`	np.ndarray
`TRIMA`	`TRIMA(real, timeperiod=30)`	np.ndarray
`T3`	`T3(real, timeperiod=5, vfactor=0.7)`	np.ndarray
`BBANDS`	`BBANDS(real, timeperiod=5, nbdevup=2.0, nbdevdn=2.0, matype=0)`	Tuple[3]
`SAR`	`SAR(high, low, acceleration=0.02, maximum=0.2)`	np.ndarray
`MIDPOINT`	`MIDPOINT(real, timeperiod=14)`	np.ndarray
`MIDPRICE`	`MIDPRICE(high, low, timeperiod=14)`	np.ndarray

Momentum Indicators

Function	Signature	Returns
`RSI`	`RSI(real, timeperiod=14)`	np.ndarray
`MACD`	`MACD(real, fastperiod=12, slowperiod=26, signalperiod=9)`	Tuple[3]
`STOCH`	`STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)`	Tuple[2]
`ADX`	`ADX(high, low, close, timeperiod=14)`	np.ndarray
`CCI`	`CCI(high, low, close, timeperiod=14)`	np.ndarray
`WILLR`	`WILLR(high, low, close, timeperiod=14)`	np.ndarray
`MFI`	`MFI(high, low, close, volume, timeperiod=14)`	np.ndarray
`MOM`	`MOM(real, timeperiod=10)`	np.ndarray
`ROC`	`ROC(real, timeperiod=10)`	np.ndarray
`ROCP`	`ROCP(real, timeperiod=10)`	np.ndarray
`ROCR`	`ROCR(real, timeperiod=10)`	np.ndarray
`ROCR100`	`ROCR100(real, timeperiod=10)`	np.ndarray
`APO`	`APO(real, fastperiod=12, slowperiod=26)`	np.ndarray
`PPO`	`PPO(real, fastperiod=12, slowperiod=26)`	np.ndarray
`CMO`	`CMO(real, timeperiod=14)`	np.ndarray
`TRIX`	`TRIX(real, timeperiod=30)`	np.ndarray
`ULTOSC`	`ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)`	np.ndarray
`AROON`	`AROON(high, low, timeperiod=14)`	Tuple[2]
`AROONOSC`	`AROONOSC(high, low, timeperiod=14)`	np.ndarray
`BOP`	`BOP(open, high, low, close)`	np.ndarray

Volatility

Function	Signature	Returns
`ATR`	`ATR(high, low, close, timeperiod=14)`	np.ndarray
`NATR`	`NATR(high, low, close, timeperiod=14)`	np.ndarray
`TRANGE`	`TRANGE(high, low, close)`	np.ndarray

Volume

Function	Signature	Returns
`OBV`	`OBV(close, volume)`	np.ndarray
`AD`	`AD(high, low, close, volume)`	np.ndarray
`ADOSC`	`ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)`	np.ndarray

Statistics

Function	Signature	Returns
`STDDEV`	`STDDEV(real, timeperiod=5, nbdev=1.0)`	np.ndarray
`VAR`	`VAR(real, timeperiod=5)`	np.ndarray
`LINEARREG`	`LINEARREG(real, timeperiod=14)`	np.ndarray
`LINEARREG_SLOPE`	`LINEARREG_SLOPE(real, timeperiod=14)`	np.ndarray
`LINEARREG_INTERCEPT`	`LINEARREG_INTERCEPT(real, timeperiod=14)`	np.ndarray
`LINEARREG_ANGLE`	`LINEARREG_ANGLE(real, timeperiod=14)`	np.ndarray
`TSF`	`TSF(real, timeperiod=14)`	np.ndarray
`BETA`	`BETA(high, low, timeperiod=5)`	np.ndarray
`CORREL`	`CORREL(high, low, timeperiod=30)`	np.ndarray

Price Transform

Function	Signature	Returns
`AVGPRICE`	`AVGPRICE(open, high, low, close)`	np.ndarray
`MEDPRICE`	`MEDPRICE(high, low)`	np.ndarray
`TYPPRICE`	`TYPPRICE(high, low, close)`	np.ndarray
`WCLPRICE`	`WCLPRICE(high, low, close)`	np.ndarray

Math Operators

Function	Signature	Returns
`MIN`	`MIN(real, timeperiod=30)`	np.ndarray
`MAX`	`MAX(real, timeperiod=30)`	np.ndarray
`SUM`	`SUM(real, timeperiod=30)`	np.ndarray
`MINMAX`	`MINMAX(real, timeperiod=30)`	Tuple[2]
`MIDPOINT`	`MIDPOINT(real, timeperiod=14)`	np.ndarray

Hilbert Transform

Function	Signature	Returns
`HT_DCPERIOD`	`HT_DCPERIOD(real)`	np.ndarray
`HT_DCPHASE`	`HT_DCPHASE(real)`	np.ndarray
`HT_TRENDMODE`	`HT_TRENDMODE(real)`	np.ndarray
`HT_TRENDLINE`	`HT_TRENDLINE(real)`	np.ndarray
`HT_PHASOR`	`HT_PHASOR(real)`	Tuple[2]
`HT_SINE`	`HT_SINE(real)`	Tuple[2]

Pattern Recognition

All 61 candlestick pattern functions are available:

CDL_DOJI, CDL_HAMMER, CDL_ENGULFING, etc.
Signature: CDL_*(open, high, low, close)
Returns: np.ndarray with +100 (bullish), -100 (bearish), or 0 (none)

Input Types

All functions accept:

numpy.ndarray
pandas.Series
list

import numpy as np
import pandas as pd
from techkit import talib_compat as ta

# All work
ta.SMA(np.array([1, 2, 3]), 2)
ta.SMA(pd.Series([1, 2, 3]), 2)
ta.SMA([1, 2, 3], 2)

Type Hints

TechKit includes type stubs for full IDE support.

# Type checking works
from techkit import SMA, RSI
import numpy as np

sma: SMA = SMA(20)
result = sma.update(100.0)  # result: IndicatorResult
values: np.ndarray = sma.calculate(np.array([...]))

Working with Pandas

import pandas as pd
import numpy as np
from techkit import SMA, RSI, MACD, BBANDS

# Load data
df = pd.read_csv('ohlcv.csv')

# Add indicators
df['SMA_20'] = SMA(20).calculate(df['close'].values)
df['SMA_50'] = SMA(50).calculate(df['close'].values)
df['RSI'] = RSI(14).calculate(df['close'].values)

# Multi-output
macd, signal, hist = MACD().calculate(df['close'].values)
df['MACD'] = macd
df['MACD_Signal'] = signal
df['MACD_Hist'] = hist

upper, middle, lower = BBANDS().calculate(df['close'].values)
df['BB_Upper'] = upper
df['BB_Middle'] = middle
df['BB_Lower'] = lower

# Generate signals
df['Signal'] = np.where(
    (df['RSI'] < 30) & (df['close'] < df['BB_Lower']),
    'BUY',
    np.where(
        (df['RSI'] > 70) & (df['close'] > df['BB_Upper']),
        'SELL',
        'HOLD'
    )
)

Performance Tips

Batch over incremental for backtesting:

   # Fast
   values = SMA(20).calculate(large_array)
   
   # Slower
   sma = SMA(20)
   values = [sma.update(p).value for p in large_array]

Reuse indicator instances:

   sma = SMA(20)
   for symbol in symbols:
       sma.reset()
       values = sma.calculate(data[symbol])

Use NumPy arrays not lists:

   # Fast
   prices = np.array(price_list)
   
   # Slower  
   prices = price_list  # Will be converted internally

Version Information

import techkit
print(techkit.__version__)  # "1.2.1"

Python API Reference

Installation

Quick Reference

Object-Oriented API

TA-Lib Compatible API

Module: techkit

Result Classes

IndicatorResult

MACDResult

BBandsResult

StochResult

Base Indicator Class

Indicator Classes

Moving Averages

Momentum Indicators

Volatility Indicators

Volume Indicators

Statistics Functions

Risk Metrics

Volatility Models

Pattern Recognition

Candlestick Patterns (61 patterns)

Chain Class

Module: techkit.talib_compat

Migration from TA-Lib

Function Reference

Overlap Studies

Momentum Indicators

Volatility

Volume

Statistics

Price Transform

Math Operators

Hilbert Transform

Pattern Recognition

Input Types

Type Hints

Working with Pandas

Performance Tips

Version Information

Module: `techkit`

Module: `techkit.talib_compat`