In this guide, you'll learn how to build and backtest a data-driven copyright trading strategy using Token Metrics API — the same API that powers Token Metrics' own AI indices and moonshot alerts.
???? Why Backtesting Matters in copyright
copyright markets are volatile, narrative-driven, and full of noise. Backtesting allows you to:
- Validate your trading hypotheses
- Measure strategy performance across cycles
- Identify weaknesses before risking capital
- Simulate real-world trading conditions with historical data
Without backtesting, you’re just guessing.
???? Step 1: Define Your Strategy Logic
First, decide the rules your trading agent will follow. Common strategies include:
✅ Momentum Strategy
Buy when a token's Trader Grade exceeds 85 and sell when it drops below 60.
✅ Signal Strategy
Buy tokens that trigger a Bullish Signal, sell on a Bearish Signal or grade deterioration.
✅ Narrative Strategy
Buy top tokens in trending narratives with increasing Trader Grades and active signals.
✅ Hybrid Moonshot Strategy
Buy low-cap tokens with:
- Trader Grade > 80
- Bullish Signal
- Appears in /moonshots/live
Define:
- Entry conditions
- Exit conditions
- Holding period or trigger for re-evaluation
???? Step 2: Access the Token Metrics API
Create an account and get your API key:
???? Token Metrics API Portal
Then plug into the MCP Server for standardized queries across tools like ChatGPT, Claude, Cursor, and Windsurf.
???? MCP GitHub
Authentication and rate limits are handled seamlessly.
???? Step 3: Query Historical Data
Use the /backtest/strategy endpoint (coming soon) or simulate backtests with time series from:
✅ /grades/trader/historical
Returns Trader Grade for a token across time
✅ /signals/bullish/history and /signals/bearish/history
Returns past signal timestamps and strength
✅ /price/history
Pulls OHLCV data to simulate performance
✅ /tokens/fundamentals/history
Track how token fundamentals evolve
???? Step 4: Simulate Strategy Over Time
Write a simple backtest script (Python, JS, etc.) to iterate through historical periods:
for day in backtest_range:
grade = get_trader_grade("TOKENX", date=day)
signal = get_signal("TOKENX", date=day)
if grade > 85 and not holding:
buy_price = get_price("TOKENX", date=day)
holding = True
elif grade < 60 and holding:
sell_price = get_price("TOKENX", date=day)
profit = sell_price - buy_price
log_trade(buy_price, sell_price, profit)
holding = False
Track:
- Entry/exit points
- ROI per trade
- Max drawdown
- Strategy win rate
- Sharpe ratio (for risk-adjusted returns)
???? Step 5: Visualize Results
Use your tool of choice (Pandas, Excel, Looker Studio) to chart:
- Equity curve over time
- ROI by token
- Signal hit rate
- Grade thresholds vs. price movement
This helps validate if your thresholds (e.g., grade >85) truly correlate with performance.
???? Step 6: Optimize and Repeat
Now iterate:
- Change grade thresholds
- Adjust holding durations
- Introduce filters like volume, market cap, or token sector
- Add narrative filters or moonshot flags
You’ll be amazed how a few tweaks can drastically change performance.
???? Example Strategy: Moonshot Momentum
Buy Condition:
- Trader Grade > 85
- Appears in /moonshots/live
- Bullish signal active
Sell Condition:
- Trader Grade < 65 OR Bearish signal triggers
Time Frame:
- Simulate daily re-evaluation for 90 days
Results:
- Tokens: 25
- Avg. ROI per trade: 48%
- Win rate: 70%
- Max drawdown: -21%
This simple strategy can now be automated with:
- A Discord alert bot
- A dashboard in Tome
- A ChatGPT plugin
- Or direct execution via DEX APIs
???? Pro Tip: Layer Multiple Signals
Want better results? Combine indicators:
- Use Trader Grade for momentum confirmation
- Use Investor Grade for long-term trend filtering
- Add Narrative relevance for macro alignment
- Limit to Moonshots for asymmetric upside
This creates smarter agents with lower noise and stronger conviction.
???? Monetize Your Strategy
Once backtested, you can:
- Deploy it in your own trading agent
- Package it as a signal for your followers
- Build a subscription-based research tool
- Create a public dashboard that updates live
Use the same endpoints, just switch to real-time mode.
???? Rate Limits & $TMAI Savings
Token Metrics offers flexible API plans:
| Plan | Monthly Calls | Use Case |
| Free | 5,000 | Testing & backtesting |
| Advanced | 50,000 | Live trading strategies |
| Premium/VIP | 500,000+ | Full automation, trading bots |
Stake or pay with $TMAI to get up to 35% discount on API usage.
???? Build Fast, Iterate Faster
Thanks to the Token Metrics MCP Server, your entire backtest → build → deploy cycle happens in one place:
- Code in Cursor
- Test in Windsurf
- Query via ChatGPT
- Present in Tome
No need to rewrite code or deal with broken schemas.
???? Final Thoughts
In 2025, profitable copyright trading agents aren’t built on hunches. They’re backed by AI, powered by real data, and refined through backtesting.
The Token Metrics API makes it easy to:
- Query historical grades and signals
- Simulate trades
- Measure strategy performance
- Optimize parameters for maximum ROI
With the MCP Server, you can build, test, and deploy in any tool — instantly.
Start your free trial, plug in your API key, and build smarter trading agents today.