A ready-to-run AI trading agent that scans 100+ assets, checks geopolitical risk, and makes BUY/SELL decisions — powered by real market data.
Last month, my AI agent told me to buy Sugar futures. Three weeks later: +16.5%.
I didn't write a single line of trading logic. The agent did everything — checked geopolitical risk, analyzed weather anomalies i Brazil, cross-referenced USDA crop data, and decided to go long. I just ran the script.
Here's how to build the same agent in 10 lines of Python.
The Agent
from north7 import North7Client
client = North7Client() # free: no key needed for risk/regime
risk = client.risk()
regime = client.regime()
print(f"Risk: {risk.risk_index}/100 ({risk.label})")
print(f"Regime: {regime.regime}")
signals = client.signals() # needs API key
best = max(signals, key=lambda s: s.risk_score or 0)
print(f"Top signal: {best.ticker} {best.action} (score {best.risk_score})")
That's it. Install with pip install north7, run it, get a trading decision.
What it actually does
Behind those 10 lines, the API scans 80+ assets across stocks, commodities, crypto, and forex. It pulls from:
- 70+ data sources in 30+ languages
- NOAA weather data (drought, flooding, harvest impact)
- USDA crop reports and EIA energy stats
- Geopolitical intelligence (sanctions, trade wars, conflicts)
- Technical momentum and sector rotation
The Risk Index (0-100) tells you how dangerous the market is right now. The Regime (BULL/BEAR/SIDEWAYS/CRISIS) tells you the trend.
Signals tell you what to trade.
The full agent
The complete trading agent adds AI analysis and decision logic:
from north7 import North7Client
client = North7Client()
# 1. Market context
risk = client.risk()
regime = client.regime()
# 2. Get signals + pick the best
signals = client.signals()
best = max(signals, key=lambda s: s.risk_score or 0)
# 3. AI deep-dive on the top pick
analysis = client.analysis(best.ticker)
# 4. Decision
if best.risk_score >= 70 and best.action == "BUY":
print(f"BUY {best.ticker} — {analysis.summary[:200]}")
elif best.risk_score >= 70 and best.action == "SELL":
print(f"SELL {best.ticker}")
else:
print("HOLD — no strong signal")
Run it as a cron job
# Every morning at 08:00 UTC
0 8 * * * python agent.py >> trades.log 2>&1
Or as a webhook agent that sends Telegram alerts:
from north7 import North7Client
import requests, time
client = North7Client()
while True:
signals = client.signals()
for s in signals:
if s.risk_score >= 75:
requests.post(f"https://api.telegram.org/bot{TOKEN}/sendMessage",
data={"chat_id": CHAT, "text": f"ALERT: {s.action} {s.ticker} (score {s.risk_score})"})
time.sleep(300) # check every 5 min
Free endpoints (no API key)
You can start without signing up:
# Risk Index — how dangerous is the market right now?
curl https://north7.ai/v1/risk
# Market Regime — BULL, BEAR, SIDEWAYS, or CRISIS?
curl https://north7.ai/v1/regime
For signals and analysis, get a free API key — 1,000 credits, no credit card.
MCP for AI agents
If you're building with Claude, GPT, or any LLM that supports Model Context Protocol:
{
"mcpServers": {
"north7": {
"command": "npx",
"args": ["-y", "@anthropic/mcp-remote", "https://north7.ai/v1/mcp"]
}
}
}
Your AI agent can now call get_risk_index(), get_signals(), get_analysis() etc. as tools.
Why this works
Most financial APIs give you raw data — price feeds, candlesticks, order books. You still need to build the logic that turns data
into decisions.
NORTH7 gives you decision-ready signals. The AI has already analyzed the data, cross-referenced sources, and assigned confidence
scores. Your agent just needs to act on them.
The Sugar trade? The agent spotted a +174% rainfall anomaly in Sao Paulo (threatening Brazil's harvest) combined with -99%
precipitation in India. It cross-referenced historical patterns and found that similar setups led to +8-16% moves within 3 weeks. The
signal came out at $17.40. Sugar hit $20.27.
Get started
pip install north7
- GitHub: ready-to-run examples
- API Documentation
- Python SDK on PyPI
tags: python, ai, trading, api
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