Artificial intelligence, particularly large language models (LLMs) like ChatGPT, Claude, or Gemini, is transforming how traders operate. These tools assist in strategy development, market analysis, and automated code generation. The rise of AI-driven trading solutions is evident, with platforms like GitHub hosting thousands of ChatGPT-related projects—many focused on trading.
This guide demonstrates how to leverage ChatGPT and Alpaca’s Trading API to build a Python trading bot using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) indicators. By the end, you’ll deploy the bot on AWS EC2 for automated execution via cron jobs.
Building an RSI and MACD Algorithmic Trading Bot
Step 1: Define Your Trading Bot Objective
Start by outlining the bot’s goals with ChatGPT:
- Automate strategy execution without manual intervention.
- Use RSI and MACD indicators to define entry/exit rules.
- Deploy on AWS EC2 for continuous operation.
Example Prompt:
"You’re an expert trader and Python developer. Teach me how to build an automated trading bot using alpaca-py and AWS EC2. Outline the architecture, essential components, and development steps."
Step 2: Choose Your Tech Stack
- Development Tools: macOS Terminal, VS Code, GitHub.
- AI Model: ChatGPT (advanced versions recommended for finance tasks).
- Cloud Hosting: AWS EC2 (Ubuntu instance).
- Trading API: Alpaca’s Python SDK.
Step 3: Understand RSI and MACD Indicators
ChatGPT can list popular technical indicators and their typical configurations:
- RSI (14-period): Identifies overbought (>70) or oversold (<30) conditions.
- MACD (12-26-9): Tracks momentum via EMA crossovers.
Prompt:
"List popular RSI/MACD entry/exit signals for mid-term trading."
Step 4: Design Entry and Exit Rules
Buy Signals:
- RSI rebounds above 30 (oversold bounce).
- MACD line crosses above the signal line.
- MACD line is above the zero line.
Sell Signals:
- RSI drops below 70 (overbought reversal).
- MACD line crosses below the signal line.
- MACD line falls under the zero line.
Step 5: Write Pseudocode
Convert your strategy into pseudocode with ChatGPT’s help:
# Entry: RSI < 30 and MACD crossover within 5 bars
if not position_open and rsi > 30 and macd_crossover:
order = submit_order(symbol="TQQQ", side="buy")
set_stop_loss(order, 2%) # Risk management 👉 Explore Alpaca’s GitHub for complete code examples
Step 6: Deploy on AWS EC2
- Set Up EC2: Launch an Ubuntu instance and SSH into it.
Schedule with Cron:
CRON_TZ=America/New_York 30 9 * * 1-5 python3 /path/to/strategy.py >> /path/to/trade_log.txt- Monitor Logs: Check
trade_log.txtfor execution details.
Key Benefits of Trading Bots
- 24/5 Operation: Trade even when you’re inactive.
- Speed: Execute orders faster than manual trading.
- Backtesting: Validate strategies with historical data.
Risks:
- Technical failures (e.g., API downtime).
- Overfitting strategies to past data.
👉 Start with Alpaca’s Paper Trading API
FAQ
Q: Which AI trading bot is best for beginners?
A: Start with Python-based bots using Alpaca’s API—it’s beginner-friendly and supports paper trading.
Q: How do I monitor my bot’s performance?
A: Use AWS CloudWatch for system metrics and review trade_log.txt for strategy execution details.
Q: Can I use this bot for cryptocurrencies?
A: Yes, but Alpaca Crypto requires separate registration and carries additional risks.
Disclaimer: Trading involves risks. Past performance doesn’t guarantee future results. Test strategies thoroughly in a paper trading environment before live deployment.
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