> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vectrade.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Examples

> Copy-paste examples for common VecTrade use cases in Python, TypeScript, and cURL.

# Examples

The [vectrade-examples](https://github.com/VecTrade-io/vectrade-examples) repository contains runnable code samples for every major use case.

## Repository Structure

```
vectrade-examples/
├── python/
│   ├── quickstart.py          # First API call
│   ├── streaming_ai.py        # Stream AI analysis
│   ├── portfolio_tracker.py   # Live portfolio dashboard
│   ├── screener.py            # Custom stock screener
│   └── webhook_server.py      # Flask webhook receiver
├── typescript/
│   ├── quickstart.ts          # First API call
│   ├── edge-function.ts       # Vercel Edge streaming
│   ├── discord-bot.ts         # Discord price alerts
│   └── ai-agent.ts            # AI agent with tools
├── curl/
│   └── examples.sh            # Raw HTTP examples
└── notebooks/
    ├── technical_analysis.ipynb
    └── earnings_calendar.ipynb
```

## Python Quickstart

```python theme={null}
from vectrade import VecTrade

client = VecTrade()

# Get a real-time quote
quote = client.quotes.get("AAPL")
print(f"{quote.symbol}: ${quote.price:.2f} ({quote.change_pct:+.2f}%)")

# Batch multiple tickers
batch = client.quotes.batch(["AAPL", "GOOGL", "MSFT", "NVDA"])
for q in batch:
    print(f"  {q.symbol}: ${q.price:.2f}")

# AI analysis with streaming
for chunk in client.ai.stream("What's the bull case for NVDA?"):
    print(chunk.text, end="", flush=True)
```

## TypeScript Quickstart

```typescript theme={null}
import { VecTrade } from "@vectrade/sdk";

const client = new VecTrade();

// Real-time quote
const quote = await client.quotes.get("AAPL");
console.log(`${quote.symbol}: $${quote.price}`);

// Screen for undervalued large-caps
const results = await client.screener.screen({
  marketCap: { min: 10_000_000_000 },
  pe: { max: 15 },
  sector: "Technology",
});

for (const stock of results) {
  console.log(`${stock.symbol} — P/E: ${stock.pe}, Market Cap: $${stock.marketCap}B`);
}
```

## AI Agent with Tool Use

```typescript theme={null}
import { createVecTrade } from "@vectrade/ai-provider";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const vt = createVecTrade();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools: vt.tools(),
  maxSteps: 5,
  prompt: "Compare AAPL and MSFT fundamentals. Which is better value?",
});

console.log(text);
```

## MCP Integration (Claude Desktop)

```json theme={null}
{
  "mcpServers": {
    "vectrade": {
      "command": "npx",
      "args": ["-y", "@vectrade/mcp-server"],
      "env": { "VECTRADE_API_KEY": "vq_live_..." }
    }
  }
}
```

Then ask Claude: *"What's TSLA trading at? Show me the options chain expiring this Friday."*

## Webhook Receiver (Python)

```python theme={null}
from flask import Flask, request
from vectrade import verify_webhook

app = Flask(__name__)
WEBHOOK_SECRET = "whsec_..."

@app.route("/webhook", methods=["POST"])
def handle():
    event = verify_webhook(
        payload=request.data,
        headers=request.headers,
        secret=WEBHOOK_SECRET,
    )

    if event.type == "price_alert.triggered":
        print(f"🚨 {event.data.symbol} hit ${event.data.price}")

    return "", 200
```

## Jupyter Notebook

```python theme={null}
# Technical analysis notebook
from vectrade import VecTrade
import pandas as pd

client = VecTrade()
technicals = client.technicals.get("AAPL", indicators=["RSI", "MACD", "BB"])

df = pd.DataFrame({
    "RSI": [technicals.rsi.value],
    "MACD": [technicals.macd.value],
    "Signal": [technicals.macd.signal],
    "BB Upper": [technicals.bollinger.upper],
    "BB Lower": [technicals.bollinger.lower],
})
df.style.highlight_max(axis=1)
```

## Run the Examples

```bash theme={null}
git clone https://github.com/VecTrade-io/vectrade-examples.git
cd vectrade-examples

# Python
pip install vectrade
python python/quickstart.py

# TypeScript
npm install
npx tsx typescript/quickstart.ts
```

## Bot Trading & AI Agent Examples

The [`use-cases/ai-agent-trading/`](https://github.com/VecTrade-io/vectrade-examples/tree/main/use-cases/ai-agent-trading) directory contains complete working examples for bot trading:

| Example                      | Description                                        |
| ---------------------------- | -------------------------------------------------- |
| `python_openai_agent.py`     | Build your own GPT-4o agent that trades via MCP    |
| `typescript_claude_agent.ts` | Claude-powered agent using Anthropic SDK + MCP     |
| `cron_strategy_bot.py`       | Scheduled mean-reversion strategy (no AI required) |
| `python/bot_trading.py`      | Direct Bot API client class                        |
| `typescript/bot-trading.ts`  | Direct Bot API in TypeScript                       |

<Note>
  VecTrade does NOT host or run AI models. You bring your own LLM (or use no AI at all).
  The MCP server exposes trading tools that any MCP-compatible agent can discover and use.
</Note>

See the [Bot Trading Guide](/guides/bot-trading) for full API reference.

<Card title="vectrade-examples on GitHub" icon="github" href="https://github.com/VecTrade-io/vectrade-examples">
  Full source code with CI-tested examples for every SDK.
</Card>