Copy Trading Platform

┌─────────────────────────────────────────────────────────┐
│              Copy Trading Platform                       │
│                                                         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │
│  │ Signal Engine │  │ Backtest     │  │ Subscriber   │   │
│  │ /v1/screener  │  │ /v1/ohlc    │  │ Dashboard    │   │
│  │ /v1/multi     │  │ Historical   │  │ /v1/summary  │   │
│  └──────┬───────┘  └──────────────┘  └──────────────┘   │
│         │                                               │
│         ▼                                               │
│  ┌──────────────────────────┐                           │
│  │ Strategy output: EURUSD  │                           │
│  │ RSI: 28 (Oversold)       │──────▶ Push to           │
│  │ MACD: Bullish crossover  │       subscribers        │
│  │ Bias: Bullish (82%)      │       (Telegram,         │
│  └──────────────────────────┘        app, email)       │
│                                                         │
│                    TickAtlas API                     │
└─────────────────────────────────────────────────────────┘

import requests

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

def generate_signals():
    """Scan market for copy trading signals."""

    # Scan for oversold conditions across all symbols. The screener filters
    # one indicator by min_val / max_val and returns data.results.
    screener = requests.get(f"{BASE}/screener", headers=headers,
        params={"indicator": "RSI_14", "max_val": 30, "timeframe": "H4"}).json()

    signals = []
    for match in screener["data"]["results"]:
        symbol = match["symbol"]

        # Confirm with multi-indicator check. Values arrive as bare floats
        # under data.data[symbol], keyed by canonical indicator name.
        multi = requests.get(f"{BASE}/multi", headers=headers,
            params={
                "symbols": symbol,
                "indicators": "RSI_14,MACD_hist",
                "timeframe": "H4"
            }).json()

        indicators = multi["data"]["data"][symbol]

        # Require confluence: RSI oversold + MACD histogram above zero.
        # The API returns the current snapshot only — no previous value —
        # so keep your own history if you want a crossover instead.
        if indicators["RSI_14"] < 30 and indicators["MACD_hist"] > 0:

            # Get the rule-based summary for signal context
            summary = requests.get(f"{BASE}/summary", headers=headers,
                params={"symbol": symbol}).json()

            signals.append({
                "symbol": symbol,
                "direction": "BUY",
                "rsi": indicators["RSI_14"],
                "macd_hist": indicators["MACD_hist"],
                "bias": summary["data"]["bias"],
                "confidence": summary["data"]["confidence"],
                "context": summary["data"]["summary"]
            })

    return signals

# Generate and distribute signals
for signal in generate_signals():
    print(f"SIGNAL: {signal['direction']} {signal['symbol']}")
    print(f"  RSI: {signal['rsi']:.1f}, bias: {signal['bias']} ({signal['confidence']:.0%})")
    # Push to subscribers via your notification system...