Built-in Indicators

ProAlgoTrader Core comes with a comprehensive set of built-in technical indicators, all optimized for high-performance trading using the Polars data processing library.

Overview

All built-in indicators inherit from the base Indicator class and provide:

• ✅ High Performance: Built on Polars for fast data processing
• ✅ Consistent API: Uniform interface across all indicators
• ✅ Data Access: data property and get_data() method for easy value retrieval
• ✅ Build Method: Direct access to raw calculations
• ✅ TradingView Support: Many indicators have TradingView-compatible versions
• ✅ Easy Integration: Seamless integration with strategies and charts

Data Access

All indicators expose two main ways to access their calculated values:

Data Property

# Get full DataFrame with all indicator data
indicator_data = rsi.data
print(indicator_data)

# Access specific columns
rsi_values = rsi.data.select("rsi")
print(rsi_values)

Get Data Method

# Get current RSI value (row 0 = most recent)
current_rsi = await rsi.get_data(0, "rsi")
print(f"Current RSI: {current_rsi}")

# Get previous RSI value (row 1 = previous value)
previous_rsi = await rsi.get_data(1, "rsi")
print(f"Previous RSI: {previous_rsi}")

# Get current indicator data as Series
current_data = await rsi.get_data(0)
print(current_data)

Debugging Data Structure

# Print DataFrame structure
print(rsi.data)
print(rsi.data.columns)
print(rsi.data.dtypes)

Indicator Categories

📈 Momentum Indicators

Momentum indicators help identify the strength and direction of price movements.

RSI (Relative Strength Index)

from proalgotrader_core.indicators import RSI

# Standard RSI
rsi = RSI(timeperiod=14)

# Get values
rsi_value = bar.get_indicator_value("rsi")

Parameters:

• timeperiod (int): Period for RSI calculation (default: 14)

Usage:

• Oversold: RSI < 30 (potential buy signal)
• Overbought: RSI > 70 (potential sell signal)
• Divergence: Price making new highs while RSI making lower highs

MACD (Moving Average Convergence/Divergence)

from proalgotrader_core.indicators import MACD

macd = MACD(fastperiod=12, slowperiod=26, signalperiod=9)

# Get values
macd_line = bar.get_indicator_value("macd")
macd_signal = bar.get_indicator_value("macd_signal")
macd_histogram = bar.get_indicator_value("macd_histogram")

Parameters:

• fastperiod (int): Fast EMA period (default: 12)
• slowperiod (int): Slow EMA period (default: 26)
• signalperiod (int): Signal line period (default: 9)

Usage:

• Bullish: MACD line crosses above signal line
• Bearish: MACD line crosses below signal line
• Divergence: Price and MACD moving in opposite directions

ADX (Average Directional Movement Index)

from proalgotrader_core.indicators import ADX

adx = ADX(timeperiod=14)

# Get values
adx_value = bar.get_indicator_value("adx")
plus_di = bar.get_indicator_value("plus_di")
minus_di = bar.get_indicator_value("minus_di")

Parameters:

• timeperiod (int): Period for ADX calculation (default: 14)

Usage:

• Strong Trend: ADX > 25
• Weak Trend: ADX < 20
• Trend Direction: +DI > -DI (bullish), +DI < -DI (bearish)

CCI (Commodity Channel Index)

from proalgotrader_core.indicators import CCI

cci = CCI(timeperiod=14)

# Get values
cci_value = bar.get_indicator_value("cci")

Parameters:

• timeperiod (int): Period for CCI calculation (default: 14)

Usage:

• Oversold: CCI < -100
• Overbought: CCI > +100
• Mean Reversion: CCI crossing zero line

Stochastic Oscillator

from proalgotrader_core.indicators import STOCH

stoch = STOCH(fastk_period=14, slowk_period=3, slowd_period=3)

# Get values
stoch_k = bar.get_indicator_value("stoch_k")
stoch_d = bar.get_indicator_value("stoch_d")

Parameters:

• fastk_period (int): Fast %K period (default: 14)
• slowk_period (int): Slow %K period (default: 3)
• slowd_period (int): Slow %D period (default: 3)

Usage:

• Oversold: %K < 20
• Overbought: %K > 80
• Bullish Crossover: %K crosses above %D
• Bearish Crossover: %K crosses below %D

Williams %R

from proalgotrader_core.indicators import WilliamsR

williams_r = WilliamsR(timeperiod=14)

# Get values
williams_r_value = bar.get_indicator_value("williams_r")

Parameters:

• timeperiod (int): Period for Williams %R calculation (default: 14)

Usage:

• Oversold: Williams %R < -80
• Overbought: Williams %R > -20

Aroon Indicator

from proalgotrader_core.indicators import AROON

aroon = AROON(timeperiod=14)

# Get values
aroon_up = bar.get_indicator_value("aroon_up")
aroon_down = bar.get_indicator_value("aroon_down")
aroon_oscillator = bar.get_indicator_value("aroon_oscillator")

Parameters:

• timeperiod (int): Period for Aroon calculation (default: 14)

Usage:

• Strong Uptrend: Aroon Up > 70, Aroon Down < 30
• Strong Downtrend: Aroon Down > 70, Aroon Up < 30
• Consolidation: Both values between 30-70

📊 Overlap Indicators

Overlap indicators are plotted on the same chart as price data.

SMA (Simple Moving Average)

from proalgotrader_core.indicators import SMA

sma = SMA(period=20)

# Get values
sma_value = bar.get_indicator_value("sma")

Parameters:

• period (int): Period for SMA calculation (default: 20)

Usage:

• Support/Resistance: Price bouncing off SMA
• Trend Direction: Price above/below SMA
• Crossovers: Fast SMA crossing slow SMA

EMA (Exponential Moving Average)

from proalgotrader_core.indicators import EMA

ema = EMA(period=20)

# Get values
ema_value = bar.get_indicator_value("ema")

Parameters:

• period (int): Period for EMA calculation (default: 20)

Usage:

• Trend Following: More responsive than SMA
• Support/Resistance: Dynamic support/resistance levels
• Golden Cross: Fast EMA crosses above slow EMA

Bollinger Bands

from proalgotrader_core.indicators import BBANDS

bbands = BBANDS(period=20, nbdevup=2, nbdevdn=2)

# Get values
bb_upper = bar.get_indicator_value("bb_upper")
bb_middle = bar.get_indicator_value("bb_middle")
bb_lower = bar.get_indicator_value("bb_lower")

Parameters:

• period (int): Period for calculation (default: 20)
• nbdevup (float): Upper band standard deviation (default: 2)
• nbdevdn (float): Lower band standard deviation (default: 2)

Usage:

• Volatility: Band width indicates volatility
• Mean Reversion: Price touching bands
• Breakouts: Price breaking out of bands

📈 Volatility Indicators

Volatility indicators measure the rate of price changes.

ATR (Average True Range)

from proalgotrader_core.indicators import ATR

atr = ATR(timeperiod=14)

# Get values
atr_value = bar.get_indicator_value("atr")

Parameters:

• timeperiod (int): Period for ATR calculation (default: 14)

Usage:

• Stop Loss: Dynamic stop loss based on volatility
• Position Sizing: Risk-based position sizing
• Volatility Breakouts: High ATR indicates potential breakouts

📊 Volume Indicators

Volume indicators incorporate trading volume in their calculations.

OBV (On Balance Volume)

from proalgotrader_core.indicators import OBV

obv = OBV()

# Get values
obv_value = bar.get_indicator_value("obv")

Usage:

• Volume Confirmation: OBV confirming price trends
• Divergence: Price and OBV moving in opposite directions
• Breakouts: OBV breaking out of patterns

MFI (Money Flow Index)

from proalgotrader_core.indicators import MFI

mfi = MFI(timeperiod=14)

# Get values
mfi_value = bar.get_indicator_value("mfi")

Parameters:

• timeperiod (int): Period for MFI calculation (default: 14)

Usage:

• Oversold: MFI < 20
• Overbought: MFI > 80
• Volume-weighted RSI: Similar to RSI but includes volume

VWAP (Volume Weighted Average Price)

from proalgotrader_core.indicators import VWAP

vwap = VWAP()

# Get values
vwap_value = bar.get_indicator_value("vwap")

Usage:

• Intraday Trading: Key level for day trading
• Fair Value: Represents average price weighted by volume
• Support/Resistance: Price often respects VWAP levels

📈 Trend Indicators

Trend indicators help identify the direction and strength of trends.

Supertrend

from proalgotrader_core.indicators import Supertrend

supertrend = Supertrend(period=10, multiplier=3)

# Get values
supertrend_value = bar.get_indicator_value("supertrend")
supertrend_direction = bar.get_indicator_value("supertrend_direction")

Parameters:

• period (int): ATR period (default: 10)
• multiplier (float): ATR multiplier (default: 3)

Usage:

• Trend Direction: Green (bullish), Red (bearish)
• Stop Loss: Dynamic trailing stop
• Trend Following: Follows the trend direction

TradingView Indicators

Many indicators have TradingView-compatible versions that match the exact calculations used in TradingView:

Available TradingView Indicators

• RSI_TV - TradingView RSI
• MACD_TV - TradingView MACD
• ADX_TV - TradingView ADX
• STOCH_TV - TradingView Stochastic
• SMA_TV - TradingView SMA
• EMA_TV - TradingView EMA
• BBANDS_TV - TradingView Bollinger Bands
• ATR_TV - TradingView ATR

Usage Example

from proalgotrader_core.indicators import RSI_TV, MACD_TV

# TradingView-compatible indicators
rsi_tv = RSI_TV(timeperiod=14)
macd_tv = MACD_TV(fastperiod=12, slowperiod=26, signalperiod=9)

# Values match TradingView exactly
rsi_value = bar.get_indicator_value("rsi_tv")
macd_line = bar.get_indicator_value("macd_tv")

Using Indicators in Strategies

Basic Usage

from proalgotrader_core.protocols import Strategy
from proalgotrader_core.indicators import RSI, SMA, MACD

class MyStrategy(Strategy):
    def __init__(self):
        super().__init__()
        self.rsi = RSI(timeperiod=14)
        self.sma_20 = SMA(period=20)
        self.macd = MACD()

    async def on_bar(self, bar):
        rsi = bar.get_indicator_value("rsi")
        sma_20 = bar.get_indicator_value("sma_20")
        macd_line = bar.get_indicator_value("macd")
        macd_signal = bar.get_indicator_value("macd_signal")

        # Trading logic
        if rsi < 30 and bar.close > sma_20 and macd_line > macd_signal:
            await self.buy(bar.symbol, quantity=100)

Advanced Usage with Build Method

class AdvancedStrategy(Strategy):
    def __init__(self):
        super().__init__()
        self.rsi = RSI(timeperiod=14)
        self.atr = ATR(timeperiod=14)

    async def on_bar(self, bar):
        # Get raw expressions for custom calculations
        rsi_expr = self.rsi.build()
        atr_expr = self.atr.build()

        # Custom logic using raw expressions
        rsi_value = bar.get_indicator_value("rsi")
        atr_value = bar.get_indicator_value("atr")

        # Dynamic stop loss based on ATR
        stop_loss = bar.close - (2 * atr_value)
        take_profit = bar.close + (3 * atr_value)

        if rsi_value < 30:
            await self.buy(
                symbol=bar.symbol,
                quantity=100,
                stop_loss=stop_loss,
                take_profit=take_profit
            )

Performance Optimization

Indicator Caching

class OptimizedStrategy(Strategy):
    def __init__(self):
        super().__init__()
        self.rsi = RSI(timeperiod=14)
        self.sma_20 = SMA(period=20)
        self.sma_50 = SMA(period=50)

    async def on_bar(self, bar):
        # Cache indicator values to avoid recalculation
        indicators = {
            'rsi': bar.get_indicator_value("rsi"),
            'sma_20': bar.get_indicator_value("sma_20"),
            'sma_50': bar.get_indicator_value("sma_50")
        }

        # Use cached values
        if indicators['rsi'] < 30 and indicators['sma_20'] > indicators['sma_50']:
            await self.buy(bar.symbol, quantity=100)

Best Practices

1. Choose Appropriate Timeframes

# Different timeframes for different purposes
rsi_short = RSI(timeperiod=7)    # Short-term signals
rsi_medium = RSI(timeperiod=14)  # Medium-term signals
rsi_long = RSI(timeperiod=21)    # Long-term signals

2. Combine Multiple Indicators

# Don't rely on single indicators
if (rsi < 30 and
    macd_line > macd_signal and
    bar.close > sma_20):
    # Multiple confirmations
    await self.buy(bar.symbol, quantity=100)

3. Use Proper Risk Management

# Always use stop losses
atr_value = bar.get_indicator_value("atr")
stop_loss = bar.close - (2 * atr_value)

await self.buy(
    symbol=bar.symbol,
    quantity=100,
    stop_loss=stop_loss
)

4. Avoid Over-optimization

# Use standard parameters
rsi = RSI(timeperiod=14)  # Standard 14-period RSI
macd = MACD()             # Standard MACD parameters

Troubleshooting

Common Issues

1. Indicator Not Updating

   • Ensure indicator is properly initialized in __init__
   • Check that required data columns are available

2. Incorrect Values

   • Verify indicator parameters
   • Check data quality and completeness
   • Use TradingView version for exact matches

3. Performance Issues

   • Cache indicator values when possible
   • Avoid unnecessary recalculations
   • Use appropriate timeframes

Next Steps

• Custom Indicators: Learn to create your own indicators in Custom Indicators Guide
• Indicator Combinations: See Strategy Examples for advanced usage
• TradingView Integration: Read TradingView Indicators for exact matches
• Performance Optimization: Check Development Guide for optimization tips

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Need help with specific indicators? Check our Indicator Quick Reference or API Reference.