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On this page
  • 🟢 How to Activate Backtesting in TradingView
  • ⏳ How to Change the Backtest Date Range
  • 💹 How to Read the Equity Curve
  • 📊 Key Metrics Breakdown
  • 📉 Trade Analysis (Win/Loss Breakdown)
  • ⚖️ Risk / Performance Ratios
  • 📄 Trade-by-Trade Review
  • ✅ Summary (based on your test results)
  1. AI Trading Agents (Bots)
  2. 🟣 AL/AI Agent Ranger Reversor v1 | Tradefi.bot • ETH/USDT • 45M

V. Backtesting & Professional Interpretation

PreviousIV. Editable Parameters & InterpretationNextVI. Exchange Integration

Last updated 5 days ago

The AL/AI Agent Ranger Reversor v1 is not just visually optimized — it’s also designed to be fully auditable using TradingView’s native strategy tester. This allows you to simulate real-world performance with maximum realism and transparency.

This section explains how to interpret key backtest metrics and performance visualizations like a pro.


🟢 How to Activate Backtesting in TradingView

  1. Click the agent in the Pine Editor

  2. Click Add to Chart → Strategy mode activates automatically

  3. Open the Strategy Tester tab at the bottom of the screen

  4. Run the simulation using the built-in settings, or modify them for your test case


⏳ How to Change the Backtest Date Range

From the Inputs panel of the script, scroll down to:

  • Start Date

  • End Date

Set your custom time window — this will apply to all calculations (equity curve, trades, drawdown, etc.)

💡 This is ideal for testing how the bot performs across different market cycles.


💹 How to Read the Equity Curve

The equity curve represents the agent’s cumulative net profit over time. In your backtest, we saw:

  • +437.88 USD in profit

  • +437.36% return on capital

  • Drawdown: 5.91%

  • Total trades: 770

  • Win rate: 71.30%

This is a strong, consistent performance with a smooth curve and limited volatility.


📊 Key Metrics Breakdown

From the "Performance" Tab:

  • Net Profit: Total money earned after all trades

  • Gross Profit / Loss: Breakdown of all winning vs. losing trades

  • Commission Paid: Simulated cost of trading with 0.045% per trade

  • Buy & Hold Return: Shows benchmark if you had held ETH over the same period

  • Max Equity Drawdown: How much the bot risked during its worst trade window


📉 Trade Analysis (Win/Loss Breakdown)

From the "Trades Analysis" tab:

  • Total Trades: 770

  • Winning Trades: 549

  • Losing Trades: 221

  • % Profitable: 71.30%

  • Average P/L per Trade: $0.57

  • Ratio Avg Win / Avg Loss: 1.02

  • Largest Win: $24.86

  • Largest Loss: $4.72

  • Average bars in trade: 5 candles

🧠 This indicates a short-cycle scalping pattern with high-frequency reversals.


⚖️ Risk / Performance Ratios

From the "Risk / Performance Ratios" tab:

  • Sharpe Ratio: 1.612 → Great risk-adjusted return

  • Profit Factor: 2.538 → Strong overall edge (above 1.5 is considered solid)

These ratios show that the strategy generates consistent alpha with controlled drawdowns.


📄 Trade-by-Trade Review

From the "List of Trades" tab, you can inspect every trade with:

  • Entry and exit time

  • Entry and exit price

  • Trade direction (long/short)

  • Run-up and drawdown

  • Realized P/L

  • Cumulative equity

📌 Tip: Use this list to identify patterns in win streaks, reversal timing, and trailing logic effectiveness.


✅ Summary (based on your test results)

  • Net Return: +437.88 USD

  • Drawdown: 5.91%

  • Win Rate: 71.30%

  • Profit Factor: 2.538

  • Total Trades: 770

  • Consistency: High

  • Execution Risk: Low

🎯 The agent shows excellent performance in 45M ETH/USDT with low drawdown and high frequency.