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Building a Cryptocurrency Exchange Rate Monitor

#webdev #python #programming #tutorial

Building a Cryptocurrency Exchange Rate Monitor

Crypto markets run 24/7 and prices can swing 10% in minutes. In this tutorial, we will build a Python-based cryptocurrency exchange rate monitor that tracks prices across exchanges, detects arbitrage opportunities, and sends alerts.

Setup 

pip install requests pandas websocket-client
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Fetching Prices from CoinGecko

CoinGecko offers a generous free API with no key required:

# Implementation is proprietary (that IS the moat).
# Skip the build — use our ready-made Apify actor:
# see the CTA below for the link (fpr=yw6md3).
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Multi-Exchange Price Comparison

Different exchanges have different prices. This creates arbitrage opportunities:

# Implementation is proprietary (that IS the moat).
# Skip the build — use our ready-made Apify actor:
# see the CTA below for the link (fpr=yw6md3).
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Real-Time WebSocket Monitoring 

import websocket
import json

def monitor_binance_ws(symbols, callback):
    streams = "/".join([f"{s.lower()}usdt@ticker" for s in symbols])
    ws_url = f"wss://stream.binance.com:9443/ws/{streams}"

    def on_message(ws, message):
        data = json.loads(message)
        ticker = {
            "symbol": data["s"],
            "price": float(data["c"]),
            "change_pct": float(data["P"]),
            "volume": float(data["v"]),
            "timestamp": datetime.now().isoformat()
        }
        callback(ticker)

    ws = websocket.WebSocketApp(ws_url, on_message=on_message)
    ws.run_forever()

def print_ticker(ticker):
    direction = "+" if ticker["change_pct"] > 0 else ""
    print(f"{ticker['symbol']}: ${ticker['price']:.2f} ({direction}{ticker['change_pct']:.2f}%)")
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Price Alert System 

class PriceAlertMonitor:
    def __init__(self):
        self.alerts = []
        self.triggered = set()

    def add_alert(self, coin, condition, threshold):
        self.alerts.append({
            "coin": coin,
            "condition": condition,
            "threshold": threshold
        })

    def check_alerts(self, prices_df):
        triggered = []
        for alert in self.alerts:
            row = prices_df[prices_df["coin"] == alert["coin"]]
            if row.empty:
                continue
            price = row.iloc[0]["price"]
            alert_key = f"{alert['coin']}_{alert['condition']}_{alert['threshold']}"

            if alert["condition"] == "above" and price > alert["threshold"]:
                if alert_key not in self.triggered:
                    triggered.append(f"{alert['coin']} is ABOVE ${alert['threshold']:,.2f} (current: ${price:,.2f})")
                    self.triggered.add(alert_key)
            elif alert["condition"] == "below" and price < alert["threshold"]:
                if alert_key not in self.triggered:
                    triggered.append(f"{alert['coin']} is BELOW ${alert['threshold']:,.2f} (current: ${price:,.2f})")
                    self.triggered.add(alert_key)

        return triggered

monitor = PriceAlertMonitor()
monitor.add_alert("bitcoin", "below", 50000)
monitor.add_alert("ethereum", "above", 5000)
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Historical Data Collection 

# Implementation is proprietary (that IS the moat).
# Skip the build — use our ready-made Apify actor:
# see the CTA below for the link (fpr=yw6md3).
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Scaling and Reliability

For monitoring many coins across many exchanges, use ScraperAPI when scraping exchange websites that block bots. ThorData proxies help access geo-restricted exchanges. ScrapeOps monitors your data pipeline uptime.

Tips

  1. Use WebSockets for real-time data instead of polling REST APIs
  2. Cache historical data locally to reduce API calls
  3. Account for fees when calculating arbitrage opportunities
  4. Use UTC timestamps consistently across all data sources
  5. Rate limit your requests — CoinGecko allows 10-30 calls/minute on free tier

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