Predator-Prey Trade Analyzer 🦅

AI-Powered Market Manipulation Detection

Phase 1: Pattern detection, paper trading, and backtesting before going live.

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🎯 What Is This?

A trading tool that detects market manipulation patterns (pump & dumps, whale accumulation, coordinated behavior) and generates trade signals.

Phase 1 Goal: Test predictions with paper trading until we hit 60%+ accuracy, THEN consider real money.

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🚀 Quick Start

Analyze a Stock

cd src
python3 dashboard.py AAPL

Monitor Watchlist

python3 dashboard.py --watch AAPL GME TSLA

View Portfolio

python3 dashboard.py --portfolio

Update Open Trades

python3 dashboard.py --update

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📊 Features

Pattern Detection

1. Pump & Dump

• Sudden volume spike (>3x average)
• Rapid price movement (>5%)
• High social media activity
• No news catalyst
• Signal: AVOID or SHORT

2. Whale Accumulation

• Steady elevated volume
• Price stable or slow rise
• Gradual accumulation pattern
• Signal: BUY (early entry)

3. Unusual Activity

• Moderate anomalies
• Requires monitoring
• Signal: WATCH

Data Sources (All Free/Public)

• Market Data: Yahoo Finance API (OHLCV, volume)
• Social Sentiment: Reddit r/wallstreetbets (mentions, scores)
• Crypto: CoinGecko API

Paper Trading

• Track predictions without real money
• Automatic entry/exit simulation
• Win rate tracking
• Performance metrics

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📈 Example Output

======================================================================
  🦅 PREDATOR-PREY TRADE ANALYZER
  Phase 1: Pattern Detection & Paper Trading
======================================================================

──────────────────────────────────────────────────────────────────────
📊 Analyzing AAPL...
──────────────────────────────────────────────────────────────────────

💰 Current Price: $182.45
📅 Time: 2026-02-20 22:30:15

📈 Volume Profile:
  Average: 45,234,567
  Recent: 98,543,210
  Ratio: 2.18x
  ⚠️  VOLUME SPIKE DETECTED

🚀 Price Velocity:
  Avg Change: +3.45%
  Direction: UP
  ⚠️  RAPID MOVEMENT DETECTED

💬 Social Sentiment (Reddit r/wallstreetbets):
  Mentions: 45
  Total Score: 1,234
  Avg Upvote Ratio: 0.87

🔍 PATTERN DETECTION
──────────────────────────────────────────────────────────────────────
🚨 Pattern: PUMP_AND_DUMP
📊 Confidence: 82.0%
⚠️  Risk Level: CRITICAL
💡 Recommendation: AVOID
⏰ Timeframe: 30-60 minutes until dump

📋 Signals Detected:
  • Volume spike: 2.2x average
  • Rapid price movement: +3.5%
  • High social activity: 45 mentions

🎯 TRADE SIGNAL
──────────────────────────────────────────────────────────────────────
⛔ Action: AVOID
   Reason: Pump & dump detected - high risk

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🛠️ How It Works

1. Data Collection

from data_fetcher import DataFetcher

fetcher = DataFetcher()
analysis = fetcher.fetch_and_analyze("AAPL")

Fetches:

• OHLCV data (5-minute candles, last 24 hours)
• Volume profile
• Price velocity
• Social sentiment

2. Pattern Detection

from pattern_detector import PatternDetector

detector = PatternDetector()
detection = detector.analyze_pattern(analysis)

Detects:

• Coordinated pumps
• Whale accumulation
• Unusual activity

Returns:

• Pattern type
• Confidence score (0-100%)
• Risk level
• Trade recommendation

3. Trade Signal Generation

trade_signal = detector.calculate_trade_signal(detection, current_price)

Generates:

• Entry price
• Stop loss
• Take profit
• Risk/reward ratio

4. Paper Trading

from paper_trader import PaperTrader

trader = PaperTrader()
trade = trader.add_trade(
    symbol="AAPL",
    action="BUY",
    entry_price=150.00,
    stop_loss=142.50,
    take_profit=172.50,
    confidence=75.0,
    pattern="WHALE_ACCUMULATION",
    signals=["Steady volume", "Gradual uptrend"]
)

Tracks:

• Entry/exit prices
• Win/loss status
• Profit/loss percentage
• Pattern accuracy

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📊 Strategy

Phase 1: Paper Trading (Current)

Goal: Achieve 60%+ win rate

Actions:

1. Monitor watchlist continuously
2. Log all predictions
3. Track which patterns work best
4. Iterate on detection algorithms
5. Analyze false positives/negatives

Duration: 1-2 weeks minimum

Phase 2: Small Real Money (Future)

Prerequisites:

• 60%+ win rate over 100+ paper trades
• Understand why predictions succeed/fail
• Risk management rules defined

Actions:

• Start with $100-500 positions
• Strict stop losses
• Track real-world slippage
• Compare to paper trading results

Phase 3: Scale (Future)

Prerequisites:

• Profitable over 3+ months
• Risk management working
• Emotional control proven

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🎯 Detection Strategies

Strategy 1: Fade the Pump

When: Pump & dump detected
Action: Wait for dump, buy the dip
Risk/Reward: 2:1 to 3:1
Win Rate Target: 55-65%

Strategy 2: Follow the Whale

When: Whale accumulation detected
Action: Buy alongside whale, exit when distribution starts
Risk/Reward: 2:1 to 4:1
Win Rate Target: 60-70%

Strategy 3: Avoid the Trap

When: High-risk patterns detected
Action: Stay out (prevention = profit)
Value: Saves capital for better opportunities

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📂 Project Structure

predator-prey-trader/
├── src/
│   ├── data_fetcher.py      # Market data collection
│   ├── pattern_detector.py  # Manipulation detection
│   ├── paper_trader.py      # Paper trading system
│   └── dashboard.py         # CLI interface
├── data/
│   └── paper_trades.json    # Paper trading history
├── tests/                   # Unit tests (TODO)
├── docs/                    # Documentation
└── README.md

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🔧 Technical Details

Detection Thresholds

Pump & Dump:

• Volume spike: >3x average
• Price velocity: >5% in <1 hour
• Social mentions: >20 in last 24h
• Confidence: Need 2+ signals, 50%+ confidence

Whale Accumulation:

• Volume: 1.2-2.0x average (elevated but not spiking)
• Price stability: <1% average change
• Gradual trend: 0.5-3% upward over period
• Confidence: Need 2+ signals, 50%+ confidence

Data Refresh Rates

• Stock data: Every 5 minutes
• Social sentiment: Every 30 minutes (to avoid rate limits)
• Trade updates: Real-time on each watchlist check

Paper Trading Rules

• Entry: Confirmed at specified price
• Stop loss: Automatic exit if price hits
• Take profit: Automatic exit if price hits
• Time limit: 7 days (close any open trades)

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📈 Performance Metrics

Track:

• Win rate: % of trades that hit take profit
• Avg profit: Average % gain per trade
• Risk/reward: Average R:R ratio
• Pattern accuracy: Which patterns work best
• False positives: Detections that don't play out
• Missed opportunities: Real patterns we didn't catch

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⚠️ Risk Management

Phase 1 (Paper Trading):

• No real money at risk
• Test aggressive and conservative strategies
• Learn from mistakes without cost

Phase 2 (Real Trading - Future):

• Max 1-2% of capital per trade
• Always use stop losses
• Never revenge trade
• Track emotional state

Legal:

• Using public data = legal
• Pattern detection = legal
• Trading on your analysis = legal
• This is NOT insider trading
• This is NOT market manipulation

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🎯 Success Criteria

To move to Phase 2 (Real Money):

✅ 100+ paper trades logged
✅ 60%+ overall win rate
✅ Positive total profit %
✅ Understand why trades win/lose
✅ Risk management rules defined
✅ Emotional control demonstrated

Current Status: Phase 1 - Paper Trading

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🚧 Roadmap

Week 1: Data Collection

• Stock data fetcher
• Social sentiment scraper
• Volume/price analyzers
• More data sources (Twitter, news)

Week 2: Pattern Detection

• Pump & dump detector
• Whale accumulation detector
• Unusual activity detector
• Insider trading patterns
• Coordinated buying detection

Week 3: Paper Trading

• Trade tracking system
• Entry/exit simulation
• Performance metrics
• Backtesting historical data

Week 4: Visualization

• CLI dashboard
• Charts (price, volume, patterns)
• Web dashboard (optional)
• Mobile alerts (Telegram)

Phase 2: Real Trading (2+ weeks out)

• Broker API integration (Robinhood, etc.)
• Real-time order execution
• Slippage tracking
• Tax reporting

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🤝 Contributing

This is a personal trading tool. Phase 1 is about learning and iteration.

Not financial advice. Trade at your own risk.

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📄 License

MIT License

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🎉 Credits

Concept: VS7
Implementation: LG2 (OpenClaw agent)
Inspired by: Predator-prey dynamics, whale watching, and avoiding getting rekt

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Ready to hunt? 🦅

Run: python3 src/dashboard.py AAPL