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AI-Trader/QUICK_START.md

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Quick Start Guide

Get AI-Trader-Server running in under 5 minutes using Docker.

Prerequisites

Step 1: Clone Repository

git clone https://github.com/Xe138/AI-Trader-Server.git
cd AI-Trader-Server

Step 2: Configure Environment

Create .env file with your API keys:

cp .env.example .env

Edit .env and add your keys:

# Required API Keys
OPENAI_API_KEY=sk-your-openai-key-here
ALPHAADVANTAGE_API_KEY=your-alpha-vantage-key-here
JINA_API_KEY=your-jina-key-here

# Optional: Custom OpenAI endpoint
# OPENAI_API_BASE=https://api.openai.com/v1

# Optional: API server port (default: 8080)
# API_PORT=8080

Save the file.

Step 3: (Optional) Custom Model Configuration

To use different AI models than the defaults, create a custom config:

  1. Create config directory:

    mkdir -p configs

  2. Create configs/config.json:

    {
  "models": [
    {
      "name": "my-gpt-4",
      "basemodel": "openai/gpt-4",
      "signature": "my-gpt-4",
      "enabled": true
    }
  ]
}

  3. The Docker container will automatically merge this with default settings.

Your custom config only needs to include sections you want to override.

Step 4: Start the API Server

docker-compose up -d

This will:

  • Build the Docker image (~5-10 minutes first time)
  • Start the AI-Trader-Server API service
  • Start internal MCP services (math, search, trade, price)
  • Initialize the SQLite database

Wait for startup:

# View logs
docker logs -f ai-trader-server

# Wait for this message:
# "Application startup complete"
# Press Ctrl+C to stop viewing logs

Step 5: Verify Service is Running

curl http://localhost:8080/health

Expected response:

{
  "status": "healthy",
  "database": "connected",
  "timestamp": "2025-01-16T10:00:00Z"
}

If you see "status": "healthy", you're ready!

Step 6: Run Your First Simulation

Trigger a simulation for a single day with GPT-4:

curl -X POST http://localhost:8080/simulate/trigger \
  -H "Content-Type: application/json" \
  -d '{
    "start_date": "2025-01-16",
    "end_date": "2025-01-16",
    "models": ["gpt-4"]
  }'

Response:

{
  "job_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "pending",
  "total_model_days": 1,
  "message": "Simulation job created with 1 model-day tasks"
}

Save the job_id - you'll need it to check status.

Note: Both start_date and end_date are required. For a single day, set them to the same value. To simulate a range, use different dates (e.g., "start_date": "2025-01-16", "end_date": "2025-01-20").

Step 7: Monitor Progress

# Replace with your job_id from Step 5
JOB_ID="550e8400-e29b-41d4-a716-446655440000"

curl http://localhost:8080/simulate/status/$JOB_ID

While running:

{
  "job_id": "550e8400-...",
  "status": "running",
  "progress": {
    "total_model_days": 1,
    "completed": 0,
    "failed": 0,
    "pending": 1
  },
  ...
}

When complete:

{
  "job_id": "550e8400-...",
  "status": "completed",
  "progress": {
    "total_model_days": 1,
    "completed": 1,
    "failed": 0,
    "pending": 0
  },
  ...
}

Typical execution time: 2-5 minutes for a single model-day.

Step 8: View Results

curl "http://localhost:8080/results?job_id=$JOB_ID" | jq '.'

Example output:

{
  "results": [
    {
      "id": 1,
      "job_id": "550e8400-...",
      "date": "2025-01-16",
      "model": "gpt-4",
      "action_type": "buy",
      "symbol": "AAPL",
      "amount": 10,
      "price": 250.50,
      "cash": 7495.00,
      "portfolio_value": 10000.00,
      "daily_profit": 0.00,
      "holdings": [
        {"symbol": "AAPL", "quantity": 10},
        {"symbol": "CASH", "quantity": 7495.00}
      ]
    }
  ],
  "count": 1
}

You can see:

  • What the AI decided to buy/sell
  • Portfolio value and cash balance
  • All current holdings

Success! What's Next?

Run Multiple Days

curl -X POST http://localhost:8080/simulate/trigger \
  -H "Content-Type: application/json" \
  -d '{
    "start_date": "2025-01-16",
    "end_date": "2025-01-20"
  }'

This simulates 5 trading days (weekdays only).

Run Multiple Models

curl -X POST http://localhost:8080/simulate/trigger \
  -H "Content-Type: application/json" \
  -d '{
    "start_date": "2025-01-16",
    "end_date": "2025-01-16",
    "models": ["gpt-4", "claude-3.7-sonnet"]
  }'

Note: Models must be defined and enabled in configs/default_config.json.

Resume from Last Completed Date

Continue simulations from where you left off (useful for daily automation):

curl -X POST http://localhost:8080/simulate/trigger \
  -H "Content-Type: application/json" \
  -d '{
    "start_date": null,
    "end_date": "2025-01-31",
    "models": ["gpt-4"]
  }'

This will:

  • Check the last completed date for each model
  • Resume from the next day after the last completed date
  • If no previous data exists, run only the end_date as a single day

Query Specific Results

# All results for a specific date
curl "http://localhost:8080/results?date=2025-01-16"

# All results for a specific model
curl "http://localhost:8080/results?model=gpt-4"

# Combine filters
curl "http://localhost:8080/results?date=2025-01-16&model=gpt-4"

Troubleshooting

Service won't start

# Check logs
docker logs ai-trader-server

# Common issues:
# - Missing API keys in .env
# - Port 8080 already in use
# - Docker not running

Fix port conflicts:

Edit .env and change API_PORT:

API_PORT=8889

Then restart:

docker-compose down
docker-compose up -d

Health check returns error

# Check if container is running
docker ps | grep ai-trader-server

# Restart service
docker-compose restart

# Check for errors in logs
docker logs ai-trader-server | grep -i error

Job stays "pending"

The simulation might still be downloading price data on first run.

# Watch logs in real-time
docker logs -f ai-trader-server

# Look for messages like:
# "Downloading missing price data..."
# "Starting simulation for model-day..."

First run can take 10-15 minutes while downloading historical price data.

"No trading dates with complete price data"

This means price data is missing for the requested date range.

Solution 1: Try a different date range (recent dates work best)

Solution 2: Manually download price data:

docker exec -it ai-trader-server bash
cd data
python get_daily_price.py
python merge_jsonl.py
exit

Common Commands

# View logs
docker logs -f ai-trader-server

# Stop service
docker-compose down

# Start service
docker-compose up -d

# Restart service
docker-compose restart

# Check health
curl http://localhost:8080/health

# Access container shell
docker exec -it ai-trader-server bash

# View database
docker exec -it ai-trader-server sqlite3 /app/data/jobs.db

Next Steps

Need Help?

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