Trade Tracking

Trade Tracking

Simple system to track whether trades are profitable or not, with automatic logging to Obsidian or Notion.

When to Use

• User wants to track trade outcomes (profitable/unprofitable)
• User wants automatic logging to their note-taking system
• User wants simple PnL tracking, not complex portfolio analytics
• Integration with AI-Trader or other signal sources

Architecture

trade_tracker.py      # Main CLI and Trade class
trade_logger.py       # Platform-specific logging (Obsidian/Notion)
trade_config.json     # Configuration
ai_trader_integration.py  # Example integration

Quick Start

1. Track Trades

# Add a trade
python3 trade_tracker.py add AAPL buy 150.50 10

# Close a trade (auto-logs to configured platform)
python3 trade_tracker.py close AAPL 155.25

# Check profitability
python3 trade_tracker.py check
# Output: Overall: PROFITABLE ($47.50)

2. Setup Logging

Obsidian (Default)

python3 trade_tracker.py setup obsidian

Notion

python3 trade_tracker.py setup notion [page_id]

Key Design Decisions

1. Simple Over Complex

• Just track entry/exit prices and quantity
• Calculate PnL and profitability (yes/no)
• Win rate and total PnL summary
• No Sharpe ratio, drawdown, or complex metrics (user explicitly wanted simple)

2. Automatic Logging

• Trades auto-log when closed (not manual)
• Creates Obsidian notes or Notion pages automatically
• Template-based for consistency

3. Modular Design

• Separate tracker (data) from logger (output)
• Config-driven platform selection
• Easy to add new logging platforms

Configuration

trade_config.json

{
  "default_platform": "obsidian",
  "obsidian": {
    "enabled": true,
    "vault_path": "/path/to/vault",
    "notes_folder": "Trade Tracking",
    "template": "# Trade: {{symbol}}\n\n**Status:** {{status}}\n..."
  },
  "notion": {
    "enabled": true,
    "database_id": "",
    "page_id": ""
  }
}

Quick Reference

Commands

# Add trade
python trade_tracker.py add SYMBOL SIDE PRICE QUANTITY
python trade_tracker.py add AAPL buy 150.50 10

# Close trade (auto-logs)
python trade_tracker.py close SYMBOL EXIT_PRICE
python trade_tracker.py close AAPL 155.25

# Check profitability
python trade_tracker.py check              # Overall
python trade_tracker.py check AAPL         # Specific symbol

# Summary
python trade_tracker.py summary

# Setup
python trade_tracker.py setup obsidian [folder]
python trade_tracker.py setup notion [page_id]

# Test
python scripts/test_setup.py

Expected Output

# Add
Added buy 10.0 AAPL @ $150.50

# Close
Logged AAPL to Obsidian: /path/to/AAPL_2026-05-21.md
Closed AAPL @ $155.25

# Check
Overall: PROFITABLE ($47.50)

# Summary
Trade Summary:
  Total Calls: 1
  Profitable: 1
  Total PnL: $47.50
  Win Rate: 100.0%
  Overall: PROFITABLE

Pitfalls

1. Circular imports: trade_logger.py defines its own Trade class to avoid importing from trade_tracker.py. Don't try to share the class - it creates circular dependencies.

2. Missing entry_time: Trade class must have entry_time field, and add_call() must set it to datetime.now().isoformat(). Logger expects this field.

3. exit_time vs closed_at: Both fields exist but serve different purposes. closed_at is for internal tracking, exit_time is for logging. Set both in close_call().

4. Platform not configured: If default platform has no database_id/page_id (Notion) or vault_path (Obsidian), logging fails silently with warning. Always run setup first.

5. Practice trade integration: When providing practice trade recommendations from oracle-analyst, always include exact python3 trade_tracker.py add commands. Users need the specific command to log trades, not just the recommendation. See references/practice-trade-integration.md for workflow details.

Obsidian Note Template

# Trade: {{symbol}}

**Status:** {{status}}
**Entry:** {{entry_price}} @ {{entry_time}}
**Exit:** {{exit_price}} @ {{exit_time}}
**Quantity:** {{quantity}}
**PnL:** {{pnl}}
**Profitable:** {{profitable}}

## Notes
{{note}}

Philosophy

Simple > comprehensive. The user typically wants one answer: "am I profitable?" Keep it minimal. Do NOT propose QuantConnect, portfolio optimization libraries, or multi-broker integrations unless asked. Default to the simplest thing that answers "profitable or not."

AI-Trader Gap

AI-Trader (ai4trade.ai) does NOT track realized PnL, closed trade history, or win rate. See references/ai-trader-gap.md for API evidence and workaround strategy. Use this tracker as the external PnL layer for AI-Trader signals.

Integration Examples

AI-Trader Integration

from trade_tracker import TradeTracker

tracker = TradeTracker()

# Log AI-Trader signal
tracker.add_call(
    symbol="BTC",
    side="buy",
    entry_price=65000.00,
    quantity=0.1
)

# Close when ready (auto-logs to Obsidian)
tracker.close_call("BTC", 67000.00)

# Check profitability
print(tracker.check_profitability())

Custom Integration

from trade_tracker import TradeTracker

tracker = TradeTracker()

# Add trade from any source
tracker.add_call(
    symbol="NVDA",
    side="buy",
    entry_price=800.00,
    quantity=5
)

# Close at exit price
tracker.close_call("NVDA", 850.00)

# Get summary
summary = tracker.get_summary()
print(f"Win rate: {summary['win_rate']}%")
print(f"Total PnL: ${summary['total_pnl']}")

Files

Templates (copy and modify)

• templates/trade_tracker.py - Main tracking system with CLI
• templates/trade_logger.py - Logging to Obsidian/Notion
• templates/trade_config.json - Configuration

References

• references/implementation-notes.md - Pitfalls, lessons learned, and technical details

Scripts

• scripts/test_setup.py - Verify installation and configuration

Quick Setup

1. Copy templates to your project:

   cp templates/trade_tracker.py .
   cp templates/trade_logger.py .
   cp templates/trade_config.json .
   

2. Configure for your platform:

   # Edit trade_config.json with your vault path or Notion details
   

3. Run setup:

   python trade_tracker.py setup obsidian
   # or
   python trade_tracker.py setup notion [page_id]
   

4. Verify installation:

   python scripts/test_setup.py
   

Future Extensions

• Web dashboard for visualization
• Risk metrics (Sharpe, max drawdown)
• Benchmark comparison (SPY, BTC)
• Multi-portfolio support
• Real-time price integration