Option trading bot
Budget: $250 – $750 USD
Option Trading Bot Challenge: Automate the Market
**Unleash AI to Navigate Options Complexity – Compete for Profitability & Innovation!**
#### **Objective**
Build an automated trading bot that executes option strategies using real-time market data. Your bot must balance **profitability**, **risk management**, and **adaptability** in volatile conditions.
#### **Key Requirements**
1. **Strategy Scope**:
- Implement ≥1 options strategy (e.g., straddles, spreads, iron condors).
- Bot must adapt to changing volatility (e.g., IV spikes).
2. **Technical Execution**:
- Use live/paper trading APIs (e.g., Alpaca, TD Ameritrade, Interactive Brokers).
- Backtest against 2020–2023 data (COVID crash + recovery).
3. **Risk Management**:
- Max daily drawdown ≤5%.
- Define stop-loss/take-profit logic.
4. **Code & Documentation**:
- GitHub repo with clean code (Python preferred).
- PDF report explaining strategy, risk rules, and backtest results.
**Evaluation Criteria**
| **Category** | **Weight** | **Details** |
|---------------------|------------|--------------------------------------------------|
| **Profitability** | 35% | Sharpe ratio >1.5, max cumulative return. |
| **Risk Management** | 30% | Low drawdown, volatility-adjusted performance. |
| **Innovation** | 20% | Novel ML/volatility forecasting, adaptive tactics.|
| **Code Quality** | 15% | Readability, scalability, error handling. |
*Resources & Support**
- Starter kits (GitHub templates for options pricing + backtesting).
- Mentorship sessions with quants/traders.
- Workshops on:
- Volatility modeling (GARCH, Heston).
- API integration (e.g., Alpaca options).
**Submission Format**
1. GitHub link to code + documentation.
2. Backtest report (equity curve, trade log, risk metrics).
3. 3-min video demo explaining one trade decision.
**Unleash AI to Navigate Options Complexity – Compete for Profitability & Innovation!**
#### **Objective**
Build an automated trading bot that executes option strategies using real-time market data. Your bot must balance **profitability**, **risk management**, and **adaptability** in volatile conditions.
#### **Key Requirements**
1. **Strategy Scope**:
- Implement ≥1 options strategy (e.g., straddles, spreads, iron condors).
- Bot must adapt to changing volatility (e.g., IV spikes).
2. **Technical Execution**:
- Use live/paper trading APIs (e.g., Alpaca, TD Ameritrade, Interactive Brokers).
- Backtest against 2020–2023 data (COVID crash + recovery).
3. **Risk Management**:
- Max daily drawdown ≤5%.
- Define stop-loss/take-profit logic.
4. **Code & Documentation**:
- GitHub repo with clean code (Python preferred).
- PDF report explaining strategy, risk rules, and backtest results.
**Evaluation Criteria**
| **Category** | **Weight** | **Details** |
|---------------------|------------|--------------------------------------------------|
| **Profitability** | 35% | Sharpe ratio >1.5, max cumulative return. |
| **Risk Management** | 30% | Low drawdown, volatility-adjusted performance. |
| **Innovation** | 20% | Novel ML/volatility forecasting, adaptive tactics.|
| **Code Quality** | 15% | Readability, scalability, error handling. |
*Resources & Support**
- Starter kits (GitHub templates for options pricing + backtesting).
- Mentorship sessions with quants/traders.
- Workshops on:
- Volatility modeling (GARCH, Heston).
- API integration (e.g., Alpaca options).
**Submission Format**
1. GitHub link to code + documentation.
2. Backtest report (equity curve, trade log, risk metrics).
3. 3-min video demo explaining one trade decision.