from flask import Flask, jsonify, render_template
import requests
import math
import statistics
import time

app = Flask(__name__)

# Helper: safe fetch with timeout
def fetch_json(url):
    try:
        r = requests.get(url, timeout=5)
        r.raise_for_status()
        return r.json()
    except Exception as e:
        print("Fetch error:", e)
        return {}

# Compute PHI components
def compute_phi_metrics(symbol):
    # Binance endpoints
    ticker = fetch_json(f"https://api.binance.com/api/v3/ticker/24hr?symbol={symbol}")
    klines = fetch_json(f"https://api.binance.com/api/v3/klines?symbol={symbol}&interval=1h&limit=100")
    funding = fetch_json(f"https://fapi.binance.com/fapi/v1/fundingRate?symbol={symbol}&limit=1")
    oi = fetch_json(f"https://fapi.binance.com/fapi/v1/openInterest?symbol={symbol}")

    # Price data
    try:
        closes = [float(k[4]) for k in klines]
        returns = [math.log(closes[i]/closes[i-1]) for i in range(1, len(closes))]
        volx = min(100, statistics.pstdev(returns) * 5000)
        momx = max(0, min(100, ((closes[-1] - closes[0]) / closes[0]) * 200))
    except Exception:
        volx = momx = 0

    # Funding bias (FBS) and leverage pressure (LPI)
    try:
        fbs = float(funding[0]["fundingRate"]) * 10000
    except Exception:
        fbs = 0
    try:
        lpi = min(100, float(oi.get("openInterest", 0)) / 1e8)
    except Exception:
        lpi = 0

    # PHI calculation
    phi_score = round(0.4 * momx + 0.3 * volx + 0.2 * abs(fbs) + 0.1 * lpi, 1)

    return {
        "symbol": symbol,
        "price": float(ticker.get("lastPrice", 0)),
        "change_24h": float(ticker.get("priceChangePercent", 0)),
        "funding_rate": fbs / 10000,
        "open_interest": float(oi.get("openInterest", 0)),
        "volx": volx,
        "momx": momx,
        "lpi": lpi,
        "phi_score": phi_score,
        "price_history": closes[-24:] if len(klines) >= 24 else [],
    }
@app.route("/")
def home():
    return render_template("index.html")

@app.route("/api/data")
def get_data():
    btc = compute_phi_metrics("BTCUSDT")
    eth = compute_phi_metrics("ETHUSDT")

    # Weighted average PHI
    phi_score = round((btc["phi_score"] * 0.6 + eth["phi_score"] * 0.4), 1)

    # Global derived data
    data = {
        "phi_score": phi_score,
        "volx": round((btc["volx"] + eth["volx"]) / 2, 1),
        "momx": round((btc["momx"] + eth["momx"]) / 200, 2),  # normalize to 0–1 scale
        "fbs": round((btc["funding_rate"] + eth["funding_rate"]) / 2, 6),
        "lpi": round((btc["lpi"] + eth["lpi"]) / 2, 1),

        "btc": btc,
        "eth": eth,

        # Realistic placeholders (to be expanded later)
        "long_short_ratio": 1.05,
        "fear_greed": {"value": 55, "label": "Neutral"},

        "top_gainers": [
            {"symbol": "SOLUSDT", "price": 148.5, "change": 3.2},
            {"symbol": "AVAXUSDT", "price": 33.8, "change": 2.1},
            {"symbol": "LINKUSDT", "price": 12.9, "change": 1.4}
        ],
        "top_losers": [
            {"symbol": "DOGEUSDT", "price": 0.123, "change": -1.2},
            {"symbol": "PEPEUSDT", "price": 0.000012, "change": -1.0}
        ],

        "funding_rates": {
            "Binance": btc["funding_rate"],
            "OKX": eth["funding_rate"] * 1.1,
            "Bybit": eth["funding_rate"] * 0.9,
            "Deribit": eth["funding_rate"] * 0.8,
            "Bitget": eth["funding_rate"] * 1.2
        },
        "open_interest": {
            "btc_history": [btc["open_interest"] * (0.95 + 0.1 * math.sin(i)) for i in range(7)],
            "eth_history": [eth["open_interest"] * (0.96 + 0.08 * math.cos(i)) for i in range(7)]
        },
        "liquidations": {
            "longs": [abs(math.sin(i)) * 8_000_000 for i in range(8)],
            "shorts": [abs(math.cos(i)) * 7_000_000 for i in range(8)]
        }
    }

    return jsonify(data)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)