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Build an Autonomous Trading Agent with Python in 10 Minutes
From zero to a working trading agent that screens tickers, checks sentiment, verifies claims, and sets alerts. Copy-paste Python code with the Polaris SDK.
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You don't need a Bloomberg terminal to build a trading agent. With the Polaris API and Python, you can screen tickers by natural language, check sentiment, verify claims, set price alerts, and backtest strategies — all in about 50 lines of code.
This tutorial builds a complete autonomous trading agent. Every code block is copy-pasteable. You'll have a working agent in 10 minutes.
Setup
bash
pip install polaris-news httpx
python
import httpx
API_KEY = "pr_live_xxx" # Get free at thepolarisreport.com/pricing
BASE = "https://api.thepolarisreport.com"
HEADERS = {"X-API-Key": API_KEY, "Content-Type": "application/json"}Step 1: Screen for Opportunities
Ask in plain English. The NLP screener translates your intent into structured filters and returns matching tickers.
python
def screen(query: str):
"""Natural language screener — describe what you want."""
resp = httpx.post(f"{BASE}/api/v1/screener/natural",
headers=HEADERS,
json={"query": query, "limit": 10})
data = resp.json()
print(f"Interpreted: {data.get('interpreted_as')}")
for r in data.get("results", []):
print(f" {r['ticker']} price={r.get('price',0):.2f} RSI={r.get('rsi_14','?')} Sent={r.get('sentiment_7d','?')}")
return data["results"]
# Find oversold semiconductors
picks = screen("oversold semiconductor stocks near earnings")Step 2: Check Sentiment + Technicals
python
def analyze(ticker: str):
"""Get composite score + technicals for a ticker."""
score = httpx.get(f"{BASE}/api/v1/ticker/{ticker}/score",
headers=HEADERS).json()
print(f"{ticker}: signal={score['signal']} score={score['composite_score']:.3f}")
print(f" Sentiment: {score['components']['sentiment']['current_24h']}")
print(f" Momentum: {score['components']['momentum']['direction']}")
return score
for pick in picks[:3]:
analyze(pick["ticker"])Step 3: Verify Before Acting
python
def verify(claim: str):
"""Fact-check a claim before trading on it."""
resp = httpx.post(f"{BASE}/api/v1/verify",
headers=HEADERS,
json={"claim": claim})
result = resp.json()
print(f"Verdict: {result['verdict']} ({result['confidence']:.0%} confidence)")
return result
# Verify before acting
verify("NVIDIA GPU demand is accelerating")Step 4: Set Alerts
python
def set_alert(ticker: str, alert_type: str, threshold: float):
"""Set a price or RSI alert with optional webhook."""
resp = httpx.post(f"{BASE}/api/v1/alerts",
headers=HEADERS,
json={
"ticker": ticker,
"alert_type": alert_type,
"threshold": threshold,
"callback_url": "https://your-webhook.example.com/alerts"
})
alert = resp.json()
print(f"Alert set: {ticker} {alert_type} at {threshold}")
return alert
set_alert("NVDA", "price_below", 165)
set_alert("NVDA", "rsi_below", 25)Step 5: Backtest Your Strategy
python
def backtest(sector: str, entry_rsi: int = 30, exit_rsi: int = 50):
"""Backtest a simple RSI strategy."""
resp = httpx.post(f"{BASE}/api/v1/backtest",
headers=HEADERS,
json={
"strategy": {
"entry_filters": {"rsi_below": entry_rsi},
"exit_filters": {"rsi_above": exit_rsi},
"sector": sector
},
"period": "1y",
"initial_capital": 10000
})
perf = resp.json()["performance"]
print(f"Return: {perf['total_return_pct']:.1f}% | Win rate: {perf['win_rate_pct']:.0f}%")
return resp.json()
backtest("Semiconductors")Putting It Together
python
# Full autonomous loop
picks = screen("oversold tech stocks with bullish sentiment")
for pick in picks[:3]:
score = analyze(pick["ticker"])
if score["signal"] in ("buy", "strong_bullish"):
verify(f"{pick.get('entity_name',pick['ticker'])} outlook is positive")
set_alert(pick["ticker"], "price_below", pick.get("price", 0) * 0.95)
print(f" → Watching {pick['ticker']}")
backtest("Technology", entry_rsi=30, exit_rsi=55)Get Started
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