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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)
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