Indicators & Signals

Indicators are mathematical calculations based on price, volume, or other market data. They form the building blocks of trading signals.

What is a Signal?

A signal is a trigger that suggests a trading action. Signals are typically generated from indicators.

Indicator → Condition → Signal → Action
   ↓           ↓          ↓         ↓
  RSI      RSI < 30     Buy     Open long

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Trend Indicators

Moving Averages

Smooth price data to identify trends.

Simple Moving Average (SMA)

def sma(prices, window):
    return sum(prices[-window:]) / window

# Usage
sma_20 = sma(close_prices, 20)
sma_50 = sma(close_prices, 50)

# Signal: Golden Cross (SMA20 crosses above SMA50)
if sma_20 > sma_50 and prev_sma_20 <= prev_sma_50:
    signal = "BUY"

Exponential Moving Average (EMA)

Gives more weight to recent prices.

def ema(prices, window):
    multiplier = 2 / (window + 1)
    ema = prices[0]
    for price in prices[1:]:
        ema = (price - ema) * multiplier + ema
    return ema

# VecAlpha built-in
ema_20 = self.ema(20)  # In strategy class

Average Directional Index (ADX)

Measures trend strength (not direction).

ADX Value	Trend Strength
0-25	Weak or no trend
25-50	Strong trend
50-75	Very strong trend
75-100	Extremely strong

adx = self.adx(14)
if adx > 25:
    # Strong trend - use trend-following strategies
else:
    # Weak trend - consider mean-reversion

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Momentum Indicators

Relative Strength Index (RSI)

Measures speed and magnitude of price changes.

RSI = 100 - (100 / (1 + RS))
RS = Average Gain / Average Loss

RSI Range	Interpretation
> 70	Overbought
50-70	Bullish
30-50	Bearish
< 30	Oversold

rsi = self.rsi(14)

# Mean reversion signals
if rsi < 30:
    signal = "BUY"  # Oversold
elif rsi > 70:
    signal = "SELL"  # Overbought

MACD (Moving Average Convergence Divergence)

Combines trend and momentum.

macd, signal_line, histogram = self.macd(12, 26, 9)

# Crossover signal
if macd > signal_line and prev_macd <= prev_signal:
    signal = "BUY"
elif macd < signal_line and prev_macd >= prev_signal:
    signal = "SELL"

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Volatility Indicators

Bollinger Bands

Volatility-based envelopes around price.

Middle Band = 20 SMA
Upper Band = Middle + (2 × Standard Deviation)
Lower Band = Middle - (2 × Standard Deviation)

upper, middle, lower = self.bollinger(20, 2)

# Mean reversion signal
if price < lower:
    signal = "BUY"  # Price at lower band
elif price > upper:
    signal = "SELL"  # Price at upper band

Average True Range (ATR)

Measures volatility.

atr = self.atr(14)

# Use for position sizing
stop_loss_distance = 2 * atr
position_size = risk_amount / stop_loss_distance

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Volume Indicators

On-Balance Volume (OBV)

Tracks cumulative volume flow.

obv = self.obv()

# Divergence detection
if price_making_new_highs and obv_not_making_new_highs:
    signal = "POTENTIAL_REVERSAL"

Volume Weighted Average Price (VWAP)

Average price weighted by volume.

vwap = self.vwap()

# Mean reversion
if price < vwap:
    # Price below VWAP - potential buy
    signal = "BUY"

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Combining Indicators

Single indicators are rarely enough. Combine multiple for confirmation:

Example: Trend + Momentum

def generate_signal(self):
    # Trend filter
    trend = self.sma(50) > self.sma(200)
    
    # Momentum signal
    rsi = self.rsi(14)
    
    # Combined signal
    if trend and rsi < 40:  # Uptrend + oversold
        return "BUY"
    elif not trend and rsi > 60:  # Downtrend + overbought
        return "SELL"
    
    return "HOLD"

Example: Multiple Timeframes

def multi_timeframe_signal(self):
    # Higher timeframe trend
    daily_trend = self.sma(50, timeframe='1d') > self.sma(200, timeframe='1d')
    
    # Lower timeframe entry
    hourly_rsi = self.rsi(14, timeframe='1h')
    
    if daily_trend and hourly_rsi < 30:
        return "BUY"
    return "HOLD"

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Indicator Best Practices

1. Understand What You're Measuring

Category	What It Measures	Example Indicators
Trend	Direction of price movement	SMA, EMA, ADX
Momentum	Speed of price change	RSI, MACD, Stochastic
Volatility	Magnitude of price swings	ATR, Bollinger Bands
Volume	Trading activity	OBV, VWAP, Volume

2. Avoid Redundancy

Don't use multiple indicators that measure the same thing:

# REDUNDANT: All measure momentum
indicators = ['RSI', 'Stochastic', 'CCI']

# BETTER: Different aspects
indicators = ['RSI', 'MACD', 'ATR']  # Momentum + Trend + Volatility

3. Match Indicators to Strategy Type

Strategy Type	Best Indicators
Trend Following	SMA, EMA, ADX, MACD
Mean Reversion	RSI, Bollinger Bands, Stochastic
Breakout	ATR, Donchian Channels
Pairs Trading	Cointegration, Correlation

4. Parameter Selection

Common parameter ranges:

Indicator	Default	Range to Test
SMA/EMA	20, 50, 200	10-200
RSI	14	7-21
MACD	12, 26, 9	Various
Bollinger	20, 2	15-25, 1.5-2.5

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Custom Indicators

Create your own in VecAlpha:

from vecalpha import Indicator

class CustomIndicator(Indicator):
    """Custom momentum indicator."""
    
    def __init__(self, window=14):
        self.window = window
    
    def calculate(self, data):
        closes = data['close']
        volumes = data['volume']
        
        # Custom calculation
        momentum = closes.pct_change(self.window)
        volume_weight = volumes / volumes.rolling(self.window).mean()
        
        return momentum * volume_weight

# Use in strategy
custom = CustomIndicator(14)
signal_value = custom.calculate(self.data)

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Next Steps

• Backtesting Guide - Test your signals properly
• Optimization - Fine-tune parameters