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20212 min readRobinhood Stock Trading Bot
Automating stock trades with algorithmic strategies
Python
Robinhood API
Automation
The Challenge
Manual trading is emotional, time-consuming, and inconsistent. I wanted to explore algorithmic trading—could I build a system that executes trades based on data rather than gut feelings?
Key Challenges
- 1Robinhood doesn't have an official public API
- 2Market data needs to be processed in real-time
- 3Risk management is critical—one bug could be expensive
- 4Backtesting strategies requires historical data
The Solution
A Python-based trading bot that interfaces with Robinhood through an unofficial API wrapper, implementing multiple trading strategies with comprehensive logging and risk controls.
Key Features
- Multiple configurable trading strategies (momentum, mean reversion)
- Real-time market data processing
- Position sizing based on portfolio value and risk tolerance
- Comprehensive trade logging for analysis
- Paper trading mode for strategy testing
Technical Highlights
Risk Management Module
Ensures no single trade exceeds risk parameters
class RiskManager:
def __init__(self, max_position_pct=0.05, max_daily_loss_pct=0.02):
self.max_position_pct = max_position_pct
self.max_daily_loss_pct = max_daily_loss_pct
self.daily_pnl = 0
def calculate_position_size(self, portfolio_value, entry_price):
"""Calculate safe position size based on risk parameters"""
max_position_value = portfolio_value * self.max_position_pct
shares = int(max_position_value / entry_price)
return max(shares, 0)
def can_trade(self, portfolio_value):
"""Check if we've hit daily loss limit"""
max_loss = portfolio_value * self.max_daily_loss_pct
return abs(self.daily_pnl) < max_lossThe Results
Key Outcomes
- Learned the fundamentals of quantitative trading
- Built robust error handling for financial systems
- Understood the importance of logging and auditability
- Gained appreciation for the complexity of market dynamics
Lessons Learned
1
Backtesting is essential—never deploy untested strategies
2
Past performance doesn't guarantee future results
3
Unofficial APIs can break without warning
4
The market is efficient; edge cases matter