tessl/pypi-stock-indicators

Stock Indicators for Python provides financial market technical indicators from historical price quotes.

Overview

Eval results

Files

Momentum Indicators

Name: tessl/pypi-stock-indicators
Author: tessl

Oscillators that measure the rate of price change and momentum to identify overbought/oversold conditions, trend strength, and potential reversal points. These indicators typically oscillate between fixed boundaries and are essential for timing market entries and exits.

Capabilities

Relative Strength Index (RSI)

Measures the strength of winning versus losing streaks on a scale of 0 to 100, commonly used to identify overbought (>70) and oversold (<30) conditions.

def get_rsi(quotes: Iterable[Quote], lookback_periods: int = 14):
    """
    Relative Strength Index (RSI) measures strength of winning/losing streak
    over N lookback periods on a scale of 0 to 100.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods in the lookback window (defaults to 14)

    Returns:
        RSIResults[RSIResult]: Collection of RSI results
    """

RSIResult Properties:

date (datetime): Result date
rsi (Optional[Decimal]): RSI value (0-100)

Stochastic Oscillator

Momentum indicator comparing closing price to its price range over a lookback period, producing %K and %D lines with optional %J extension for KDJ analysis.

def get_stoch(quotes: Iterable[Quote], lookback_periods: int = 14, signal_periods: int = 3, 
              smooth_periods: int = 3, k_factor: float = 3, d_factor: float = 2, 
              ma_type: MAType = MAType.SMA):
    """
    Stochastic Oscillator with KDJ indexes - momentum indicator on a scale of 0 to 100.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for the Oscillator (defaults to 14)
        signal_periods (int): Smoothing period for the %D signal line (defaults to 3)
        smooth_periods (int): Smoothing period for the %K Oscillator (defaults to 3, use 1 for Fast)
        k_factor (float): K factor for %J calculation (defaults to 3)
        d_factor (float): D factor for %J calculation (defaults to 2)
        ma_type (MAType): Moving average type for smoothing (defaults to SMA)

    Returns:
        STOCHResults[STOCHResult]: Collection of Stochastic results
    """

STOCHResult Properties:

date (datetime): Result date
oscillator (Optional[Decimal]): %K oscillator value (0-100)
signal (Optional[Decimal]): %D signal line value (0-100)
percent_j (Optional[Decimal]): %J extension value for KDJ analysis

Stochastic RSI

Combines RSI and Stochastic oscillator concepts, applying stochastic calculation to RSI values for enhanced sensitivity.

def get_stoch_rsi(quotes: Iterable[Quote], rsi_periods: int = 14, stoch_periods: int = 14, 
                  signal_periods: int = 3, smooth_periods: int = 1):
    """
    Stochastic RSI applies stochastic calculation to RSI values for enhanced sensitivity.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        rsi_periods (int): Number of periods for RSI calculation (defaults to 14)
        stoch_periods (int): Number of periods for Stochastic calculation (defaults to 14)
        signal_periods (int): Smoothing period for signal line (defaults to 3)
        smooth_periods (int): Smoothing period for oscillator (defaults to 1)

    Returns:
        StochRSIResults[StochRSIResult]: Collection of Stochastic RSI results
    """

StochRSIResult Properties:

date (datetime): Result date
stoch_rsi (Optional[Decimal]): Stochastic RSI value (0-100)
percent_k (Optional[Decimal]): %K line value
percent_d (Optional[Decimal]): %D signal line value

MACD (Moving Average Convergence/Divergence)

Shows the relationship between two moving averages with signal line and histogram for trend and momentum analysis.

def get_macd(quotes: Iterable[Quote], fast_periods: int = 12, slow_periods: int = 26, 
             signal_periods: int = 9, candle_part: CandlePart = CandlePart.CLOSE):
    """
    Moving Average Convergence/Divergence (MACD) - oscillator view of two
    converging/diverging exponential moving averages.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        fast_periods (int): Number of periods in the Fast EMA (defaults to 12)
        slow_periods (int): Number of periods in the Slow EMA (defaults to 26)
        signal_periods (int): Number of periods for the Signal moving average (defaults to 9)
        candle_part (CandlePart): Selected OHLCV part (defaults to CLOSE)

    Returns:
        MACDResults[MACDResult]: Collection of MACD results
    """

MACDResult Properties:

date (datetime): Result date
macd (Optional[Decimal]): MACD line (fast EMA - slow EMA)
signal (Optional[Decimal]): Signal line (EMA of MACD)
histogram (Optional[Decimal]): MACD histogram (MACD - Signal)
fast_ema (Optional[Decimal]): Fast EMA value
slow_ema (Optional[Decimal]): Slow EMA value

Connors RSI

Multi-component momentum oscillator combining RSI, streak analysis, and rate of change for enhanced overbought/oversold signals.

def get_connors_rsi(quotes: Iterable[Quote], rsi_periods: int = 3, streak_periods: int = 2, 
                    rank_periods: int = 100):
    """
    Connors RSI - multi-component momentum oscillator combining RSI, streak, and rank analysis.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        rsi_periods (int): RSI lookback periods (defaults to 3)
        streak_periods (int): Consecutive up/down periods for streak RSI (defaults to 2)
        rank_periods (int): Periods for percentile rank of rate of change (defaults to 100)

    Returns:
        ConnorsRSIResults[ConnorsRSIResult]: Collection of Connors RSI results
    """

ConnorsRSIResult Properties:

date (datetime): Result date
rsi_close (Optional[Decimal]): RSI of closing prices
rsi_streak (Optional[Decimal]): RSI of streak values
percent_rank (Optional[Decimal]): Percentile rank component
connors_rsi (Optional[Decimal]): Combined Connors RSI value (0-100)

Rate of Change (ROC)

Measures the percentage change in price over a specified number of periods.

def get_roc(quotes: Iterable[Quote], lookback_periods: int):
    """
    Rate of Change (ROC) measures percentage change in price over lookback periods.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for rate calculation

    Returns:
        ROCResults[ROCResult]: Collection of ROC results
    """

ROCResult Properties:

date (datetime): Result date
roc (Optional[Decimal]): Rate of change as percentage
roc_sma (Optional[Decimal]): Simple moving average of ROC

Rate of Change with Band

ROC with upper and lower bands based on standard deviation for volatility context.

def get_roc_with_band(quotes: Iterable[Quote], lookback_periods: int, ema_periods: int, 
                      std_dev_periods: int):
    """
    Rate of Change with Bands - ROC with volatility bands.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for ROC calculation
        ema_periods (int): EMA periods for center line
        std_dev_periods (int): Standard deviation periods for bands

    Returns:
        ROCWBResults[ROCWBResult]: Collection of ROC with Bands results
    """

ROCWBResult Properties:

date (datetime): Result date
roc (Optional[Decimal]): Rate of change value
upper_band (Optional[Decimal]): Upper volatility band
lower_band (Optional[Decimal]): Lower volatility band

Awesome Oscillator

Bill Williams' Awesome Oscillator using the difference between 5-period and 34-period simple moving averages of midpoint prices.

def get_awesome(quotes: Iterable[Quote], fast_periods: int = 5, slow_periods: int = 34):
    """
    Awesome Oscillator - difference between fast and slow SMAs of midpoint prices.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        fast_periods (int): Fast SMA periods (defaults to 5)
        slow_periods (int): Slow SMA periods (defaults to 34)

    Returns:
        AwesomeResults[AwesomeResult]: Collection of Awesome Oscillator results
    """

AwesomeResult Properties:

date (datetime): Result date
oscillator (Optional[Decimal]): Awesome Oscillator value

Commodity Channel Index (CCI)

Momentum oscillator that identifies cyclical trends and overbought/oversold conditions.

def get_cci(quotes: Iterable[Quote], lookback_periods: int = 20):
    """
    Commodity Channel Index (CCI) - momentum oscillator for cyclical trends.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for calculation (defaults to 20)

    Returns:
        CCIResults[CCIResult]: Collection of CCI results
    """

CCIResult Properties:

date (datetime): Result date
cci (Optional[Decimal]): CCI value (typically between -100 and +100)

Chande Momentum Oscillator (CMO)

Momentum oscillator that uses both up and down price movements in its calculation, similar to RSI but with different scaling.

def get_cmo(quotes: Iterable[Quote], lookback_periods: int = 14):
    """
    Chande Momentum Oscillator (CMO) - momentum indicator using up/down movements.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for calculation (defaults to 14)

    Returns:
        CMOResults[CMOResult]: Collection of CMO results
    """

CMOResult Properties:

date (datetime): Result date
cmo (Optional[Decimal]): CMO value (-100 to +100)

Williams %R

Momentum indicator that measures overbought/oversold levels, similar to Stochastic but with inverted scale.

def get_williams_r(quotes: Iterable[Quote], lookback_periods: int = 14):
    """
    Williams %R - momentum indicator measuring overbought/oversold levels.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for calculation (defaults to 14)

    Returns:
        WilliamsResults[WilliamsResult]: Collection of Williams %R results
    """

WilliamsResult Properties:

date (datetime): Result date
williams_r (Optional[Decimal]): Williams %R value (-100 to 0)

Price Momentum Oscillator (PMO)

Momentum oscillator that double-smooths price rate of change for cleaner signals.

def get_pmo(quotes: Iterable[Quote], time_periods: int = 35, smoothing_periods: int = 20, 
            signal_periods: int = 10):
    """
    Price Momentum Oscillator (PMO) - double-smoothed rate of change oscillator.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        time_periods (int): Time periods for ROC calculation (defaults to 35)
        smoothing_periods (int): Smoothing periods for first EMA (defaults to 20)
        signal_periods (int): Signal line EMA periods (defaults to 10)

    Returns:
        PMOResults[PMOResult]: Collection of PMO results
    """

PMOResult Properties:

date (datetime): Result date
pmo (Optional[Decimal]): PMO oscillator value
signal (Optional[Decimal]): PMO signal line

Ultimate Oscillator

Multi-timeframe momentum oscillator that combines short, medium, and long-term price momentum.

def get_ultimate(quotes: Iterable[Quote], short_periods: int = 7, middle_periods: int = 14, 
                 long_periods: int = 28, short_weight: float = 4, middle_weight: float = 2, 
                 long_weight: float = 1):
    """
    Ultimate Oscillator - multi-timeframe momentum combining short, medium, and long periods.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        short_periods (int): Short timeframe periods (defaults to 7)
        middle_periods (int): Middle timeframe periods (defaults to 14)
        long_periods (int): Long timeframe periods (defaults to 28)
        short_weight (float): Weight for short timeframe (defaults to 4)
        middle_weight (float): Weight for middle timeframe (defaults to 2)
        long_weight (float): Weight for long timeframe (defaults to 1)

    Returns:
        UltimateResults[UltimateResult]: Collection of Ultimate Oscillator results
    """

UltimateResult Properties:

date (datetime): Result date
ultimate (Optional[Decimal]): Ultimate Oscillator value (0-100)

True Strength Index (TSI)

Double-smoothed momentum oscillator that reduces noise while maintaining sensitivity to price changes.

def get_tsi(quotes: Iterable[Quote], lookback_periods: int = 25, smooth_periods: int = 13, 
            signal_periods: int = 7):
    """
    True Strength Index (TSI) - double-smoothed momentum oscillator.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Lookback periods for momentum (defaults to 25)
        smooth_periods (int): First smoothing periods (defaults to 13)
        signal_periods (int): Signal line smoothing periods (defaults to 7)

    Returns:
        TSIResults[TSIResult]: Collection of TSI results
    """

TSIResult Properties:

date (datetime): Result date
tsi (Optional[Decimal]): TSI oscillator value
signal (Optional[Decimal]): TSI signal line

TRIX

Triple exponentially smoothed moving average oscillator that filters out short-term price movements.

def get_trix(quotes: Iterable[Quote], lookback_periods: int = 14, signal_periods: int = 9):
    """
    TRIX - triple exponentially smoothed moving average oscillator.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for TRIX calculation (defaults to 14)
        signal_periods (int): Signal line EMA periods (defaults to 9)

    Returns:
        TRIXResults[TRIXResult]: Collection of TRIX results
    """

TRIXResult Properties:

date (datetime): Result date
ema1 (Optional[Decimal]): First EMA value
ema2 (Optional[Decimal]): Second EMA value
ema3 (Optional[Decimal]): Third EMA value
trix (Optional[Decimal]): TRIX oscillator value (as percentage)
signal (Optional[Decimal]): TRIX signal line

Detrended Price Oscillator (DPO)

Removes trend from price data to focus on shorter-term cycles by comparing price to a displaced moving average.

def get_dpo(quotes: Iterable[Quote], lookback_periods: int):
    """
    Detrended Price Oscillator (DPO) - removes trend to focus on cycles.

    Args:
        quotes (Iterable[Quote]): Historical price quotes
        lookback_periods (int): Number of periods for moving average calculation

    Returns:
        DPOResults[DPOResult]: Collection of DPO results
    """

DPOResult Properties:

date (datetime): Result date
sma (Optional[Decimal]): Simple moving average value
dpo (Optional[Decimal]): Detrended Price Oscillator value

Usage Examples

RSI Overbought/Oversold Analysis

from stock_indicators.indicators import get_rsi

# Calculate RSI with standard 14-period setting
rsi_results = get_rsi(quotes, lookback_periods=14)

# Identify overbought/oversold conditions
for result in rsi_results:
    if result.rsi is not None:
        if result.rsi > 70:
            print(f"{result.date}: RSI {result.rsi:.2f} - Overbought")
        elif result.rsi < 30:
            print(f"{result.date}: RSI {result.rsi:.2f} - Oversold")

MACD Signal Analysis

from stock_indicators.indicators import get_macd

# Calculate MACD with standard settings
macd_results = get_macd(quotes, fast_periods=12, slow_periods=26, signal_periods=9)

# Look for MACD crossovers
previous_result = None
for result in macd_results:
    if result.macd is not None and result.signal is not None and previous_result:
        # Bullish crossover: MACD crosses above signal
        if (previous_result.macd <= previous_result.signal and 
            result.macd > result.signal):
            print(f"{result.date}: Bullish MACD crossover")
        # Bearish crossover: MACD crosses below signal  
        elif (previous_result.macd >= previous_result.signal and 
              result.macd < result.signal):
            print(f"{result.date}: Bearish MACD crossover")
    previous_result = result

Multi-Indicator Momentum Confirmation

from stock_indicators.indicators import get_rsi, get_stoch, get_cci

# Calculate multiple momentum indicators
rsi_results = get_rsi(quotes, lookback_periods=14)
stoch_results = get_stoch(quotes, lookback_periods=14)
cci_results = get_cci(quotes, lookback_periods=20)

# Look for momentum confirmation across indicators
for i in range(len(rsi_results)):
    rsi_val = rsi_results[i].rsi
    stoch_val = stoch_results[i].oscillator
    cci_val = cci_results[i].cci
    
    if rsi_val and stoch_val and cci_val:
        # Check for oversold confirmation
        oversold_signals = 0
        if rsi_val < 30: oversold_signals += 1
        if stoch_val < 20: oversold_signals += 1
        if cci_val < -100: oversold_signals += 1
        
        if oversold_signals >= 2:
            print(f"{rsi_results[i].date}: Oversold confirmation - {oversold_signals}/3 indicators")

Install with Tessl CLI