TickAtlas
Guide 10 min read · March 28, 2026

The Complete Guide to Bollinger Bands for Developers

Everything developers need to know about Bollinger Bands: the math, the API, and three proven trading strategies you can implement today.

CG
By the TickAtlas team

What Bollinger Bands Tell You

Bollinger Bands are a volatility envelope around a moving average. The bands widen when volatility increases and contract when it decreases. For a developer building trading systems, they provide three actionable data points: dynamic support (lower band), dynamic resistance (upper band), and a volatility measure (bandwidth).

The Math (So You Know What the API Computes)

  • Middle Band = 20-period Simple Moving Average (SMA)
  • Upper Band = Middle Band + (2 x 20-period Standard Deviation)
  • Lower Band = Middle Band - (2 x 20-period Standard Deviation)
  • Bandwidth = (Upper - Lower) / Middle
  • %B = (Price - Lower) / (Upper - Lower)

You do not need to compute any of this yourself. The TickAtlas API returns all five values pre-calculated.

API Response

json 
// GET /v1/indicators?symbol=EURUSD&timeframe=H1&indicators=BBANDS_20
{
  "success": true,
  "data": {
    "symbol": "EURUSD",
    "timeframe": "H1",
    "indicators": {
      "BBANDS_20": {
        "upper": 1.0892,
        "middle": 1.0856,
        "lower": 1.0820,
        "bandwidth": 0.0066
      }
    },
    "price": {
      "close": 1.0835
    },
    "timestamp": "2026-03-28T14:00:00Z"
  }
}

Strategy 1: Bollinger Bounce (Mean Reversion)

Price tends to revert to the middle band. When price touches the lower band, it often bounces back. Combine with RSI for confirmation.

python 
import requests

API_KEY = "your_api_key_here"
BASE_URL = "https://tickatlas.com/v1"

def bollinger_bounce(symbol: str) -> str:
    resp = requests.get(f"{BASE_URL}/indicators", params={
        "symbol": symbol,
        "timeframe": "H1",
        "indicators": "BBANDS_20,RSI_14"
    }, headers={"X-API-Key": API_KEY})

    data = resp.json()["data"]
    price = data["price"]["close"]
    bb = data["indicators"]["BBANDS_20"]
    rsi = data["indicators"]["RSI_14"]["value"]

    # Buy: price at/below lower band + RSI confirms oversold
    if price <= bb["lower"] * 1.002 and rsi < 35:
        return f"BUY {symbol} — bouncing off lower BB ({bb['lower']:.5f}), RSI {rsi:.1f}"

    # Sell: price at/above upper band + RSI confirms overbought
    if price >= bb["upper"] * 0.998 and rsi > 65:
        return f"SELL {symbol} — rejected at upper BB ({bb['upper']:.5f}), RSI {rsi:.1f}"

    return "HOLD"

Strategy 2: Bollinger Squeeze (Breakout)

When bandwidth contracts to historically low levels, a big move is coming. The squeeze does not tell you the direction — but a breakout above the upper band is bullish, and below the lower band is bearish.

python 
def bollinger_squeeze(symbol: str) -> str:
    resp = requests.get(f"{BASE_URL}/indicators", params={
        "symbol": symbol,
        "timeframe": "H4",
        "indicators": "BBANDS_20,ADX_14"
    }, headers={"X-API-Key": API_KEY})

    data = resp.json()["data"]
    price = data["price"]["close"]
    bb = data["indicators"]["BBANDS_20"]
    adx = data["indicators"]["ADX_14"]["value"]

    # Detect squeeze: low bandwidth + low ADX
    if bb["bandwidth"] < 0.003 and adx < 20:
        return f"SQUEEZE {symbol} — bandwidth {bb['bandwidth']:.4f}, ADX {adx:.1f}. Breakout imminent."

    # Breakout confirmation
    if bb["bandwidth"] > 0.005 and price > bb["upper"]:
        return f"BREAKOUT UP {symbol} — price above upper BB after squeeze"

    if bb["bandwidth"] > 0.005 and price < bb["lower"]:
        return f"BREAKOUT DOWN {symbol} — price below lower BB after squeeze"

    return "HOLD"

Strategy 3: Bollinger Band Walk (Trend Following)

In strong trends, price "walks" along the upper or lower band for extended periods. Instead of fading the band touch, you ride it.

python 
def bollinger_walk(symbol: str) -> str:
    resp = requests.get(f"{BASE_URL}/indicators", params={
        "symbol": symbol,
        "timeframe": "H1",
        "indicators": "BBANDS_20,ADX_14,MACD"
    }, headers={"X-API-Key": API_KEY})

    data = resp.json()["data"]
    price = data["price"]["close"]
    bb = data["indicators"]["BBANDS_20"]
    adx = data["indicators"]["ADX_14"]["value"]
    macd_hist = data["indicators"]["MACD"]["histogram"]

    # Strong uptrend: price near upper band + ADX strong + MACD bullish
    if adx > 25 and price > bb["middle"] and macd_hist > 0:
        if price > bb["upper"] * 0.998:
            return f"TREND BUY {symbol} — walking upper band, ADX {adx:.1f}"

    # Strong downtrend: price near lower band + ADX strong + MACD bearish
    if adx > 25 and price < bb["middle"] and macd_hist < 0:
        if price < bb["lower"] * 1.002:
            return f"TREND SELL {symbol} — walking lower band, ADX {adx:.1f}"

    return "HOLD"

Key Developer Considerations

Bandwidth is Your Volatility Metric

Track bandwidth over time. A declining bandwidth for 20+ candles signals an impending volatility expansion. Use this to prepare your strategy.

Always Combine with a Trend Filter

Use ADX to determine which Bollinger strategy to apply: mean reversion when ADX < 20, trend following when ADX > 25.

Multi-Timeframe Confirmation

A Bollinger squeeze on H4 is more significant than on M15. Check multiple timeframes before acting on a squeeze signal.

Further Reading

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