Agent 1: The Stop-Loss Agent

import requests
import anthropic
import time

client = anthropic.Anthropic()

def get_btc_price():
    url = 'https://api.binance.com/api/v3/ticker/price?symbol=BTCUSDT'
    return float(requests.get(url).json()['price'])

def get_btc_volume():
    url = 'https://api.binance.com/api/v3/ticker/24hr?symbol=BTCUSDT'
    data = requests.get(url).json()
    return float(data['volume']), float(data['priceChangePercent'])

def analyze_with_claude(price, entry_price, volume, change_pct):
    prompt = f"""Bitcoin price alert analysis:
    - Current price: ${'{'}price:,.2f{'}'}
    - Entry price:   ${'{'}entry_price:,.2f{'}'}
    - Loss from entry: {'{'}((price - entry_price) / entry_price * 100):.2f{'}'}%
    - 24h volume: {'{'}volume:,.0f{'}'} BTC
    - 24h price change: {'{'}change_pct:.2f{'}'}%

    Should I exit this position? Analyze risk and give YES/NO with brief reasoning."""

    response = client.messages.create(
        model='claude-sonnet-4-20250514',
        max_tokens=200,
        messages=[{'role': 'user', 'content': prompt}]
    )
    return response.content[0].text

def run_stop_loss_agent(entry_price: float, stop_loss_pct: float = 5.0):
    print(f'Monitoring BTC. Entry: ${'{'}entry_price:,.2f{'}'} | Stop: -{'{'}stop_loss_pct{'}'}%')
    while True:
        price = get_btc_price()
        volume, change_pct = get_btc_volume()
        loss_pct = ((price - entry_price) / entry_price) * 100

        if loss_pct <= -stop_loss_pct:
            print(f'🚨 ALERT: BTC at ${'{'}price:,.2f{'}'} — Stop-loss triggered!')
            analysis = analyze_with_claude(price, entry_price, volume, change_pct)
            print(f'Claude Analysis: {'{'}analysis{'}'}')
            break

        print(f'BTC: ${'{'}price:,.2f{'}'} | Change: {'{'}loss_pct:.2f{'}'}%')
        time.sleep(60)

# Run with your entry price and 5% stop-loss threshold
run_stop_loss_agent(entry_price=65000, stop_loss_pct=5.0)

Agent 2: The Liquidity Tracker

import requests
import anthropic

client = anthropic.Anthropic()

def get_order_book_depth(symbol='BTCUSDT', depth=50):
    url = f'https://api.binance.com/api/v3/depth?symbol={'{'}symbol{'}'}&limit={'{'}depth{'}'}'
    data = requests.get(url).json()
    return data['bids'], data['asks']

def calculate_liquidity_within_range(bids, current_price, range_pct=2.0):
    threshold = current_price * (1 - range_pct / 100)
    return sum(
        float(price) * float(qty)
        for price, qty in bids
        if float(price) >= threshold
    )

def assess_liquidity_risk(liquidity_usd: float, avg_liquidity: float):
    ratio = liquidity_usd / avg_liquidity if avg_liquidity > 0 else 0
    prompt = f"""Bitcoin order book liquidity report:
    - Current bid-side liquidity (within 2%): ${'{'}liquidity_usd:,.0f{'}'}
    - Average baseline liquidity: ${'{'}avg_liquidity:,.0f{'}'}
    - Liquidity ratio: {'{'}ratio:.2f{'}'}x normal

    Rate the flash crash risk: LOW, MEDIUM, or HIGH. Explain in 2 sentences."""

    response = client.messages.create(
        model='claude-sonnet-4-20250514',
        max_tokens=150,
        messages=[{'role': 'user', 'content': prompt}]
    )
    return response.content[0].text

# Example usage
current_price = float(requests.get(
    'https://api.binance.com/api/v3/ticker/price?symbol=BTCUSDT'
).json()['price'])
bids, asks = get_order_book_depth()
liquidity = calculate_liquidity_within_range(bids, current_price)
risk = assess_liquidity_risk(liquidity, avg_liquidity=8000000)
print(f'Liquidity: ${'{'}liquidity:,.0f{'}'} | Risk: {'{'}risk{'}'}')

Agent 3: The Sentiment Alert Bot

💡 Extend this bot with additional data sources:

import requests
import anthropic

client = anthropic.Anthropic()

def get_crypto_news(api_key: str, query='Bitcoin'):
    url = f'https://newsapi.org/v2/everything?q={'{'}query{'}'}&sortBy=publishedAt&pageSize=10&apiKey={'{'}api_key{'}'}'
    articles = requests.get(url).json().get('articles', [])
    return [a['title'] for a in articles if a.get('title')]

def analyze_sentiment(headlines: list) -> str:
    headlines_text = '\n'.join([f'- {'{'}h{'}'}' for h in headlines])
    prompt = f"""Analyze the sentiment of these Bitcoin news headlines:
{'{'}headlines_text{'}'}

Return a JSON object with:
- overall_sentiment: BULLISH, NEUTRAL, or BEARISH
- confidence: 0-100
- key_risks: list of 2-3 main risk factors if bearish
- recommendation: brief trading stance suggestion"""

    response = client.messages.create(
        model='claude-sonnet-4-20250514',
        max_tokens=300,
        messages=[{'role': 'user', 'content': prompt}]
    )
    return response.content[0].text

# Run the sentiment bot
NEWS_API_KEY = 'your_newsapi_key_here'
headlines = get_crypto_news(NEWS_API_KEY)
sentiment = analyze_sentiment(headlines)
print('=== BITCOIN SENTIMENT REPORT ===')
print(sentiment)

Dashboard Status Preview

Conclusion

Flash crashes will happen again. The only question is whether you will be watching passively or whether you will have an AI system actively protecting your position.

The three agents in this post are not complex to build — each can be deployed in an afternoon. The real cost is not building them. It is not having them when you need them.