Swing Trading

┌─────────────────────────────────────────────────────────────┐
│                   Swing Trading System                      │
│                                                             │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐   │
│  │ Daily Scan   │    │ Confluence   │    │ Position     │   │
│  │ /v1/screener │───▶│ /v1/multi    │───▶│ Manager      │   │
│  │ (D1 + H4)   │    │ (RSI+MACD+  │    │ (Entry/Exit) │   │
│  └──────────────┘    │  ADX+BB)    │    └──────┬───────┘   │
│                      └──────────────┘           │           │
│  ┌──────────────┐                               ▼           │
│  │ Summary      │───────────────────▶  Trade Decision      │
│  │ /v1/summary  │    Sentiment                  │           │
│  └──────────────┘    Confirmation               ▼           │
│                                          Broker Order API   │
└─────────────────────────────────────────────────────────────┘

import requests

API_KEY = "your_api_key"
BASE = "https://tickatlas.com/v1"
headers = {"X-API-Key": API_KEY}

def analyze_swing_setup(symbol):
    # Get the rule-based summary for overall bias. /v1/summary defaults to H1,
    # so ask for D1 explicitly to match the confluence check below.
    summary = requests.get(f"{BASE}/summary", headers=headers,
        params={"symbol": symbol, "timeframe": "D1"}).json()

    # Multi-indicator confluence on D1. /v1/multi nests the per-symbol map
    # under data.data, and every indicator is a bare float keyed by its
    # canonical name (RSI_14, MACD_hist, ADX ...).
    multi = requests.get(f"{BASE}/multi", headers=headers,
        params={
            "symbols": symbol,
            "indicators": "RSI_14,MACD_hist,ADX",
            "timeframe": "D1"
        }).json()

    indicators = multi["data"]["data"][symbol]
    rsi = indicators["RSI_14"]
    macd_hist = indicators["MACD_hist"]
    adx = indicators["ADX"]

    # Band position comes from the summary's key_values, which carry bid
    # alongside bb_upper / bb_lower on the summary timeframe.
    kv = summary["data"]["key_values"]
    bb_position = ((kv["bid"] - kv["bb_lower"])
                   / (kv["bb_upper"] - kv["bb_lower"]))

    # Confluence scoring
    bullish_signals = 0
    if rsi > 50 and rsi < 70: bullish_signals += 1
    if macd_hist > 0: bullish_signals += 1
    if adx > 25: bullish_signals += 1  # Strong trend
    if bb_position < 0.5: bullish_signals += 1  # Room to run

    bias = summary["data"]["bias"]
    confidence = summary["data"]["confidence"]

    return {
        "symbol": symbol,
        "bias": bias,
        "confidence": confidence,
        "bullish_signals": bullish_signals,
        "trade": bullish_signals >= 3 and bias == "bullish"
    }

# Scan watchlist
watchlist = ["EURUSD", "GBPUSD", "AUDUSD", "USDJPY", "XAUUSD"]
for sym in watchlist:
    result = analyze_swing_setup(sym)
    if result["trade"]:
        print(f"SWING SETUP: {sym} ({result['bullish_signals']}/4 conditions, bias: {result['bias']})")