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Algo Trading - Quick Start

What You Built

A complete algorithmic trading system that:

✅ Screens for stock candidates
✅ Detects pump signals before they happen
✅ Sizes positions smartly (8% per stock max)
✅ Manages exits with profit targets, stop losses, time limits
✅ Trades automatically with Webull paper trading
✅ Protects portfolio with guardrails (25% risky max)

Portfolio Guardrails (Already Built In)

Guardrail	Limit	Protection
Max per stock	8%	Doesn't bet too much on one stock
Max risky trades	25%	Doesn't exceed comfort zone
Max positions	10	Manageable portfolio
Min per trade	$100	Only meaningful trades
Max per trade	$2000	Reasonable position sizing

Exit Strategies (Already Built In)

When to automatically SELL:

Profit Target: +5% → EXIT
Stop Loss: -2% → EXIT
Time Limit: 5 days → EXIT
Trailing Stop: 2% from peak → EXIT
Signal Deterioration: Signal drops below 40 → EXIT

Quick Start (3 minutes)

Step 1: Run the Demo (No Setup Needed)

cd /home/gnara/TradingAgents
python algo_trading_demo.py

This demonstrates:

Position sizing (8% rule in action)
Exit strategies (profit targets, stops)
Trade validation
Complete flow

Step 2: Set Up Webull (Paper Trading)

Go to https://webull.com
Create account
In settings: Enable Paper Trading
Get your Trading PIN (usually 6 digits)

Step 3: Configure for Webull

Create .env file in project root:

WEBULL_EMAIL=your_email@example.com
WEBULL_PASSWORD=your_password
WEBULL_PIN=123456

Or set environment variables:

export WEBULL_EMAIL="your_email@example.com"
export WEBULL_PASSWORD="your_password"
export WEBULL_PIN="123456"

Step 4: Run Real Trading

Option A - In Python script:

from algo_trading_workflow import AlgoTradingBot
import os

bot = AlgoTradingBot(
    portfolio_cash=10000.0,
    paper_trading=True,
    selected_analysts=["market", "social"],
    webull_email=os.getenv("WEBULL_EMAIL"),
    webull_password=os.getenv("WEBULL_PASSWORD"),
    webull_pin=os.getenv("WEBULL_PIN"),
)

# Run 10 iterations (50 minutes with 5-min intervals)
bot.run(iterations=10, interval_seconds=300)

bot.print_summary()

Option B - Command line (after setting env vars):

# Edit bottom of algo_trading_workflow.py to use env vars
python algo_trading_workflow.py

How It Works (Example)

MINUTE 0: Market opens
  → Screening agent finds NVDA is trending
  → Pump detection: score 82/100
  → Buy signal (>70) ✓
  → Position size: 8% * 82% = 6.5% of portfolio
  → Enter: 4 shares @ $150 = $600

MINUTE 5-25: Monitor position
  → Current price: $155 (+3.3%)
  → Still holding (no exit signal yet)

MINUTE 30: Price jumps to $158
  → P/L: +5.3% ✓ HIT PROFIT TARGET
  → EXIT: Sell 4 shares @ $158
  → Profit: $32 (5.3%)
  → Cash restored to portfolio

MINUTE 35: Ready for next trade
  → Repeat for next signal

Customization Examples

Conservative (Lower Risk)

bot = AlgoTradingBot(portfolio_cash=10000)

# Tighter exits
bot.exit_strategy.config.profit_target_pct = 3.0   # Take profit at 3%
bot.exit_strategy.config.stop_loss_pct = 1.5       # Stop at 1.5%
bot.exit_strategy.config.max_hold_days = 3         # Hold max 3 days

# Smaller positions
bot.portfolio_manager.max_position_pct = 0.05      # 5% per stock
bot.portfolio_manager.max_risky_pct = 0.15         # 15% risky
bot.portfolio_manager.max_positions = 5            # Max 5 positions

Aggressive (Higher Risk)

bot = AlgoTradingBot(portfolio_cash=10000)

# Wider exits (hold for bigger gains)
bot.exit_strategy.config.profit_target_pct = 10.0  # Hold for 10%
bot.exit_strategy.config.stop_loss_pct = 5.0       # Stop at 5%
bot.exit_strategy.config.max_hold_days = 10        # Hold 10 days

# Larger positions
bot.portfolio_manager.max_position_pct = 0.12      # 12% per stock
bot.portfolio_manager.max_risky_pct = 0.40         # 40% risky
bot.portfolio_manager.max_positions = 15           # Max 15 positions

Monitoring Your Trades

Check Status

status = bot.get_status()
print(f"Iteration: {status['iteration']}")
print(f"Portfolio: ${status['portfolio']['total_value']:.2f}")
print(f"Positions: {status['portfolio']['num_positions']}")

View Trades

for trade in bot.trade_log:
    if trade['action'] == 'BUY':
        print(f"BUY {trade['shares']}x {trade['ticker']} @ ${trade['price']:.2f}")
    else:
        print(f"SELL {trade['shares']}x {trade['ticker']} @ ${trade['price']:.2f} "
              f"(P/L: ${trade['profit']:.2f})")

Print Summary

bot.print_summary()
# Shows total portfolio value, P/L, number of trades, etc.

Save State

bot.save_state("my_trading_results.json")
# Load later to resume or analyze

Core Files

File	Purpose
`algo_trading_demo.py`	← START HERE - Run this first
`algo_trading_workflow.py`	Main bot orchestrator
`tradingagents/strategy/portfolio_manager.py`	Position sizing & limits
`tradingagents/strategy/exit_strategy.py`	Profit/loss management
`tradingagents/strategy/trade_validator.py`	Pre-flight validation
`tradingagents/agents/trader/paper_trading.py`	Webull integration
`ALGO_TRADING_GUIDE.md`	Full detailed documentation

Common Issues

Q: Bot places too many trades

Lower profit target: exit_strategy.config.profit_target_pct = 3.0
Increase hold time: exit_strategy.config.max_hold_days = 7

Q: Positions too small

Increase max position: portfolio_manager.max_position_pct = 0.12
Lower min position: portfolio_manager.min_position_size = 50.0

Q: Too risky

Lower max risky: portfolio_manager.max_risky_pct = 0.15
Tighter stop loss: exit_strategy.config.stop_loss_pct = 1.0

Q: Webull login fails

Check email/password in .env
Check Trading PIN is correct
Check 2FA is enabled on account
Try logging in to webull.com manually first

Safety Tips

Always test in demo mode first - Run algo_trading_demo.py to verify logic
Start with small capital - Use $500-$1000 first, not $10,000
Monitor first trades - Watch your first 5-10 trades closely
Adjust based on results - If win rate is <50%, tighten stops
Never go all-in - Always keep reserve cash (25% minimum)
Use paper trading first - Don't use real money until you're profitable

Next Steps

✅ Run algo_trading_demo.py - See how it works
✅ Read ALGO_TRADING_GUIDE.md - Full technical details
✅ Set up Webull account - Get paper trading ready
✅ Configure bot - Customize strategy for your risk appetite
✅ Test with small capital - $500-$1000 in paper trading
✅ Monitor daily - Track results, adjust rules
✅ Scale up - Only after consistent profits

Questions?

Check ALGO_TRADING_GUIDE.md for:

Detailed architecture diagrams
All configuration options
Code examples
Troubleshooting guide

Remember: Algo trading is powerful but risky. Paper trading lets you learn safely. Start small, monitor closely, and only scale after proven results.

Good luck! 🚀

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