Bitcoin Technical Analysis API: RSI, MACD, and More for BTC

import requests

API_KEY = "your_api_key_here"
BASE_URL = "https://tickatlas.com/v1"
HEADERS = {"X-API-Key": API_KEY}

def btc_snapshot(timeframe: str = "H4") -> dict:
    """Get a comprehensive BTC indicator snapshot."""
    resp = requests.get(f"{BASE_URL}/indicators", params={
        "symbol": "BTCUSD",
        "timeframe": timeframe,
    }, headers=HEADERS)
    return resp.json()["data"]

def btc_momentum_signal() -> dict:
    """Multi-indicator BTC momentum strategy."""
    data = btc_snapshot("H4")
    ind = data["indicators"]
    price = data["ohlcv"]["close"]

    rsi = ind["RSI_14"]
    macd_hist = ind["MACD_hist"]
    adx = ind["ADX"]
    stoch_k = ind["Stochastic_K"]
    atr = ind["ATR_14"]

    score = 0  # -3 to +3

    # RSI momentum
    if rsi > 55: score += 1
    elif rsi < 45: score -= 1

    # MACD momentum
    if macd_hist > 0: score += 1
    elif macd_hist < 0: score -= 1

    # Stochastic momentum
    if stoch_k > 50: score += 1
    elif stoch_k < 50: score -= 1

    # Determine signal
    if score >= 2 and adx > 25:
        signal = "BUY"
    elif score <= -2 and adx > 25:
        signal = "SELL"
    else:
        signal = "HOLD"

    return {
        "signal": signal,
        "price": price,
        "score": score,
        "rsi": rsi,
        "adx": adx,
        "atr": atr,
        "stop_distance": atr * 1.5,  # Tighter for BTC momentum
    }

result = btc_momentum_signal()
print(f"BTC Signal: {result['signal']}")
print(f"Price: \${result['price']:,.2f} | Score: {result['score']}")
print(f"RSI: {result['rsi']:.1f} | ADX: {result['adx']:.1f}")

def btc_volatility_regime() -> str:
    """Classify the current BTC volatility regime."""
    data = btc_snapshot("D1")  # Daily timeframe
    ind = data["indicators"]
    adx = ind["ADX"]
    atr = ind["ATR_14"]

    bandwidth = ind["BB_width"]

    if bandwidth < 0.05 and adx < 20:
        return "COMPRESSION"  # Squeeze — breakout incoming
    elif bandwidth > 0.15 and adx > 35:
        return "EXPANSION"  # High volatility trending
    elif adx > 25:
        return "TRENDING"  # Normal trend
    else:
        return "RANGING"  # Low momentum

regime = btc_volatility_regime()
print(f"BTC Regime: {regime}")

# Strategy routing
if regime == "COMPRESSION":
    print("  -> Prepare for breakout. Watch for BB band breach.")
elif regime == "EXPANSION":
    print("  -> Ride the trend. Use trailing stops.")
elif regime == "TRENDING":
    print("  -> Trend-follow with MACD confirmation.")
else:
    print("  -> Range trade with RSI mean reversion.")

def btc_multi_tf_analysis() -> dict:
    """Analyze BTC across H1, H4, and D1 timeframes."""
    analysis = {}

    for tf in ["H1", "H4", "D1"]:
        data = btc_snapshot(tf)
        ind = data["indicators"]
        analysis[tf] = {
            "rsi": ind["RSI_14"],
            "macd_direction": "bullish" if ind["MACD_hist"] > 0 else "bearish",
            "adx": ind["ADX"],
            "trend": "up" if ind["RSI_14"] > 50 else "down"
        }

    # Check alignment
    directions = [a["macd_direction"] for a in analysis.values()]
    aligned = len(set(directions)) == 1

    return {
        "timeframes": analysis,
        "aligned": aligned,
        "consensus": directions[0] if aligned else "mixed",
        "strength": "STRONG" if aligned else "WEAK"
    }

mtf = btc_multi_tf_analysis()
print(f"BTC Multi-TF: {mtf['consensus']} ({mtf['strength']})")
for tf, data in mtf["timeframes"].items():
    print(f"  {tf}: RSI {data['rsi']:.1f}, MACD {data['macd_direction']}, ADX {data['adx']:.1f}")