Gold vs Forex: Using Correlation Data for Better Trades

import requests
import numpy as np

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

def fetch_closes(symbol: str, timeframe: str, limit: int = 50) -> list[float]:
    """Fetch closing prices for correlation calculation."""
    resp = requests.get(
        f"{BASE_URL}/ohlcv",
        headers={"X-API-Key": API_KEY},
        params={"symbol": symbol, "timeframe": timeframe, "limit": limit},
    )
    resp.raise_for_status()
    candles = resp.json()["data"]["candles"]
    return [c["close"] for c in candles]

def calculate_correlation(closes_a: list, closes_b: list) -> float:
    """Pearson correlation coefficient between two price series."""
    if len(closes_a) != len(closes_b):
        min_len = min(len(closes_a), len(closes_b))
        closes_a = closes_a[:min_len]
        closes_b = closes_b[:min_len]
    return round(float(np.corrcoef(closes_a, closes_b)[0, 1]), 3)

# Calculate correlations
gold = fetch_closes("XAUUSD", "D1", 50)
pairs = {
    "EURUSD": fetch_closes("EURUSD", "D1", 50),
    "GBPUSD": fetch_closes("GBPUSD", "D1", 50),
    "AUDUSD": fetch_closes("AUDUSD", "D1", 50),
    "USDJPY": fetch_closes("USDJPY", "D1", 50),
    "USDCAD": fetch_closes("USDCAD", "D1", 50),
}

print("Gold (XAUUSD) Correlations -- 50-Day Rolling:")
for pair, closes in pairs.items():
    corr = calculate_correlation(gold, closes)
    print(f"  {pair}: {corr:+.3f}")

def detect_divergence(
    sym_a: str, sym_b: str, timeframe: str,
    expected_correlation: float, threshold: float = 0.3
) -> dict:
    """Detect when two normally-correlated assets diverge."""
    # Get RSI for both (as a proxy for recent momentum)
    rsi_a = requests.get(
        f"{BASE_URL}/indicator", headers={"X-API-Key": API_KEY},
        params={"symbol": sym_a, "indicator": "RSI_14", "timeframe": timeframe},
    ).json()["data"]["values"]["rsi"]

    rsi_b = requests.get(
        f"{BASE_URL}/indicator", headers={"X-API-Key": API_KEY},
        params={"symbol": sym_b, "indicator": "RSI_14", "timeframe": timeframe},
    ).json()["data"]["values"]["rsi"]

    # Normalize RSI to -1 to +1 scale
    norm_a = (rsi_a - 50) / 50
    norm_b = (rsi_b - 50) / 50

    if expected_correlation > 0:
        # Positive correlation: both should move same direction
        divergence = abs(norm_a - norm_b)
    else:
        # Negative correlation: should move opposite
        divergence = abs(norm_a + norm_b)

    return {
        "pair_a": f"{sym_a} (RSI: {rsi_a:.1f})",
        "pair_b": f"{sym_b} (RSI: {rsi_b:.1f})",
        "divergence_score": round(divergence, 2),
        "is_divergent": divergence > threshold,
    }

# Check if gold and AUDUSD are diverging
result = detect_divergence("XAUUSD", "AUDUSD", "D1", expected_correlation=0.7)
if result["is_divergent"]:
    print(f"DIVERGENCE: {result['pair_a']} vs {result['pair_b']}")
    print(f"Score: {result['divergence_score']}")