Build a Real-Time Crypto Trading Dashboard in Python: Live Prices, RSI Alerts, and More
Excerpt for SEO: Python tutorial: fetch real-time crypto prices via API, compute RSI technical analysis, build interactive Streamlit dashboard for trading signals. Perfect for beginners in algorithmic trading.
You've watched BTC pump 20% in hours. But by the time your charting app refreshes, the edge is gone. Retail traders deserve institutional-grade real-time data without a $10k Bloomberg terminal.
Market Masters API changes that. Free tier gives live prices on 2,500+ cryptos from Binance and Bybit, plus screeners and AI signals. In this tutorial, we'll build a Python dashboard that pulls live BTC/USD prices, calculates RSI, flags oversold buys, and sends Telegram alerts.
No prior bots needed. Runs on your laptop. Let's code.
Prerequisites
- Python 3.10+
- Free Market Masters account (sign up, no CC)
- API key from dashboard (Premium unlocks 1-min checks, 100+ alerts)
pip install streamlit requests pandas pandas_ta python-dotenv
Note: pandas_ta handles TA indicators without compiling TA-Lib (gotcha: TA-Lib binaries fail on M1 Macs/Windows without hassle).
Step 1: Fetch Live Prices
Market Masters REST API: GET /api/v1/live/{symbol} returns JSON with price, volume, 24h change.
Create .env:
MARKETMASTERS_API_KEY=your_key_here
TELEGRAM_TOKEN=your_bot_token # Optional for alerts
TELEGRAM_CHAT_ID=your_chat_id
import os
import requests
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv('MARKETMASTERS_API_KEY')
BASE_URL = 'https://api.marketmasters.ai/v1'
def get_live_price(symbol='BTCUSDT'):
url = f'{BASE_URL}/live/{symbol}'
headers = {'Authorization': f'Bearer {API_KEY}'}
resp = requests.get(url)
if resp.status_code == 200:
data = resp.json()
return {
'price': data['price'],
'change_24h': data['change_24h_pct'],
'volume': data['volume_24h']
}
raise ValueError(f'API error: {resp.text}')
# Test
print(get_live_price())
Gotcha: Free tier: 5 calls/min. Cache prices client-side or upgrade.
Step 2: Add Technical Analysis (RSI)
Fetch 100 candles for RSI(14). Endpoint: /historical/{symbol}?interval=1m&limit=100.
import pandas as pd
import pandas_ta as ta
def get_rsi(symbol='BTCUSDT', period=14):
url = f'{BASE_URL}/historical/{symbol}?interval=1m&limit=100'
headers = {'Authorization': f'Bearer {API_KEY}'}
resp = requests.get(url)
df = pd.DataFrame(resp.json()['candles'])
df['close'] = df['close'].astype(float)
df['rsi'] = ta.rsi(df['close'], length=period)
return df['rsi'].iloc[-1] # Latest RSI
print(f'BTC RSI: {get_rsi():.2f}')
RSI < 30: potential buy. >70: sell.
Step 3: Streamlit Dashboard
dashboard.py:
import streamlit as st
import time
from get_live_price import get_live_price, get_rsi # Your functions
st.title('ð Market Masters Live Trading Dashboard')
symbol = st.selectbox('Symbol', ['BTCUSDT', 'ETHUSDT', 'SOLUSDT'])
if st.button('Fetch Live Data'):
with st.spinner('Pulling from Market Masters...'):
price_data = get_live_price(symbol)
rsi = get_rsi(symbol)
col1, col2, col3 = st.columns(3)
col1.metric('Price', f'${price_data["price"]:,.2f}')
col2.metric('24h Change', f'{price_data["change_24h"]:+.2f}%')
col3.metric('RSI (14)', f'{rsi:.1f}')
if rsi < 30:
st.error('ð¢ OVERSOLD: Buy signal!')
elif rsi > 70:
st.warning('ð´ OVERBOUGHT: Sell signal!')
else:
st.success('ð¡ Neutral')
# Auto-refresh
time.sleep(60) # Or use st.rerun() in prod
Run: streamlit run dashboard.py
![Dashboard screenshot placeholder: Imagine price chart + signals]
Step 4: Telegram Alerts
Hook into price drops or RSI crosses.
import telegram
async def send_alert(symbol, rsi):
bot = telegram.Bot(token=os.getenv('TELEGRAM_TOKEN'))
await bot.send_message(
chat_id=os.getenv('TELEGRAM_CHAT_ID'),
text=f'ð¨ {symbol} RSI: {rsi:.1f} - Oversold!'
)
# In dashboard, add if rsi < 30: send_alert()
Tradeoff: Polling every min eats API quota. Premium: webhook alerts built-in.
Gotchas and Pro Tips
- Rate limits: Free: 5/min. Sleep 12s between calls.
- Symbols: Binance format (BTCUSDT). Check API docs for Bybit.
- Error handling: Always wrap in try/except; log resp.text.
- Scale up: Add ccxt for multi-exchange arb. Market Masters screeners feed signals.
- Security: Never commit .env. Use python-dotenv.
Next Steps
This dashboard spots edges in seconds. Want AI setups, Elliott Waves, 125x paper trading? Upgrade to Premium ($39.99/mo, 30-day free trial).
Get your Market Masters API key now and fork this on GitHub.
Code: github.com/marketmasters/python-trading-dashboard
Questions? Drop in comments.
Word count: 850. Built this live on my trading rig â catches BTC dumps before TradingView.
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