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Understanding Backtest Results

After your backtest completes, you'll see a comprehensive results page with detailed performance metrics, risk analysis, and trade history. This guide explains how to interpret each section.

Results Page Overview​

The backtest results page is divided into several key sections:

  1. Performance Comparison - Bot vs Buy & Hold
  2. Risk Metrics - Volatility and drawdown analysis
  3. Trading Statistics - Win rates and trade counts
  4. Strategy Configuration - Parameters used
  5. Backtest Information - Market and timeframe details
  6. Price Chart - Visual representation with signals
  7. Trade History - Detailed log of all trades

1. Performance Comparison​

This section compares your strategy against a simple buy-and-hold approach.

Bot Performance​

Total Return

  • What it shows: Overall profit or loss percentage
  • Formula: (Final Value - Starting Value) / Starting Value × 100%
  • Example: Started with 10,000 USDT, ended with 12,500 USDT = +25% return

Annual Return (APY)

  • What it shows: Annualized rate of return
  • Why it matters: Allows comparison across different time periods
  • Formula: ((Final Value / Starting Value)^(365 / Days Elapsed) - 1) × 100%
  • Example: 25% return over 6 months = ~57.8% annualized

Value Change

  • Shows: Starting balance → Ending balance
  • Currency: Quote asset (USDT, BTC, IDR, etc.)
  • Example: 10,000.00 USDT ➜ 12,500.00 USDT

Duration

  • Days Elapsed: Total number of trading days
  • Time Range: Start date → End date

Buy & Hold Performance (HODL)​

What it represents: Results if you simply bought at the start and held until the end, without any trading.

Why compare?

  • Trading strategies should ideally outperform simple buy-and-hold
  • If your strategy underperforms HODL, you're paying fees for worse results
  • However, HODL has higher volatility and larger drawdowns

Calculation:

Starting: Buy asset at first candle's opening price
Ending: Sell asset at last candle's closing price
No trades in between

Performance Comparison Indicator​

At the bottom of this section, you'll see either:

✅ Strategy outperformed buy-and-hold by X.XX%

  • Your strategy generated better returns than simple holding
  • Green badge indicates positive outperformance

❌ Strategy underperformed buy-and-hold by X.XX%

  • Simple buy-and-hold would have been more profitable
  • Red badge indicates underperformance

Important: Outperforming HODL doesn't guarantee future success, but underperforming HODL suggests the strategy needs improvement or isn't suitable for the tested market conditions.

2. Risk Metrics​

These metrics help you understand the risk characteristics of your strategy.

Win Rate​

Definition: Percentage of profitable trades out of total trades

Formula: (Winning Trades / Total Trades) × 100%

Interpretation:

  • 70%+: Excellent - Most trades are profitable
  • 60-70%: Very Good - Strong performance
  • 50-60%: Good - Slight edge over random
  • 40-50%: Moderate - Needs large wins to compensate
  • Below 40%: Poor - Strategy likely needs adjustment

Note: High win rate doesn't guarantee profitability if winning trades are small and losing trades are large.

Max Drawdown​

Definition: Largest peak-to-trough decline in portfolio value

Formula: (Peak Value - Trough Value) / Peak Value × 100%

Example:

Portfolio reaches $15,000 (peak)
Then drops to $12,000 (trough)
Max Drawdown = ($15,000 - $12,000) / $15,000 = 20%

Interpretation:

  • 0-10%: Low risk - Very conservative
  • 10-20%: Moderate risk - Acceptable for most traders
  • 20-30%: High risk - Requires strong risk tolerance
  • 30%+: Very high risk - Only for aggressive traders

Why it matters: This is the maximum loss you would have experienced if you started at the worst possible time. It indicates your pain threshold.

Sharpe Ratio​

Definition: Risk-adjusted return metric (return per unit of volatility)

Formula: (Average Return - Risk-Free Rate) / Standard Deviation of Returns

Interpretation:

  • Above 3: Excellent - Exceptional risk-adjusted returns
  • 2-3: Very Good - Strong risk-adjusted performance
  • 1-2: Good - Adequate compensation for risk
  • 0-1: Poor - Not enough return for the risk taken
  • Below 0: Very Poor - Losing money

Why it matters: A strategy with 30% return and high volatility might be worse than one with 20% return and low volatility.

Profit Factor​

Definition: Ratio of gross profits to gross losses

Formula: Total Gains / Total Losses

Interpretation:

  • Above 2: Excellent - Gains are 2x losses
  • 1.5-2: Very Good - Strong profitability
  • 1.2-1.5: Good - Profitable but moderate
  • 1-1.2: Marginal - Barely profitable
  • Below 1: Unprofitable - Losses exceed gains

Example:

Total gains from winning trades: $5,000
Total losses from losing trades: $2,000
Profit Factor = $5,000 / $2,000 = 2.5

Sweet spot: Profit factor of 1.5-2.5 indicates a robust strategy.

Volatility​

Definition: Standard deviation of returns, measuring portfolio value fluctuation

Interpretation:

  • Low (0-10%): Stable returns, low risk
  • Moderate (10-20%): Normal volatility
  • High (20-30%): Significant fluctuations
  • Very High (30%+): Extreme swings

Context matters: High volatility with high returns might be acceptable; high volatility with low returns is problematic.

3. Trading Statistics​

Detailed breakdown of your trades.

Trade Counts​

Total Trades

  • Number of complete buy-sell cycles
  • Example: Buy → Sell = 1 trade
  • More trades ≠ better (consider fees)

Winning Trades

  • Trades that ended with profit
  • Displayed in green

Losing Trades

  • Trades that ended with loss
  • Displayed in red

Win Rate

  • Percentage of profitable trades
  • Same as in Risk Metrics section

Average Trade Performance​

Average Win

  • Average profit from winning trades
  • Currency: Quote asset (USDT, BTC, etc.)
  • Higher is better

Average Loss

  • Average loss from losing trades
  • Displayed as absolute value
  • Lower is better

Example Analysis:

Average Win: 150 USDT
Average Loss: 50 USDT
Win/Loss Ratio: 3.0

Interpretation: On average, wins are 3x larger than losses.
This allows for profitable trading even with a 40% win rate.

Extreme Values​

Largest Win

  • Single most profitable trade
  • Shows best-case scenario

Largest Loss

  • Single worst performing trade
  • Critical for risk management

Red flag: If largest loss is significantly bigger than largest win, your risk management needs improvement.

4. Strategy Configuration​

Shows the exact parameters used for this backtest.

For Preset Strategies​

Displays all configurable parameters with their values:

Example - Williams %R:

Period: 14
Oversold Level: -80
Overbought Level: -20

Each parameter shows:

  • Label: Human-readable name
  • Type: Data type (integer, float, boolean)
  • Description: What the parameter does
  • Default Value: What was used in this backtest

For Custom Strategies​

Buy Conditions

  • Lists all conditions that must be true for a buy signal
  • Shows indicator, operator, and value
  • Example: RSI(14) < 30

Sell Conditions

  • Lists all conditions for a sell signal
  • Shows indicator, operator, and value
  • Example: RSI(14) > 70

5. Backtest Configuration​

Technical details about the backtest setup.

Trading Pair: The market tested (e.g., BTC/USDT)

Timeframe: Candle interval (e.g., 1d, 4h, 1h)

Duration: Number of days tested

Initial Balance: Starting capital in quote asset

Taker Fee: Trading fee percentage used

Data Source: Exchange data used (e.g., Binance)

6. Price Chart​

Interactive candlestick chart showing:

Visual Elements​

  • Candlesticks: Historical price action
  • Buy Markers: Green indicators where strategy bought
  • Sell Markers: Red indicators where strategy sold
  • Current Position: Highlighted if still holding

How to Use​

  1. Zoom: Zoom in/out to see detail
  2. Pan: Scroll to navigate through time
  3. Hover: See exact prices and dates
  4. Click Markers: View trade details

What to Look For​

  • ✅ Buys at local lows, sells at local highs
  • ✅ Signals align with trend direction
  • ⚠️ Buying at tops (poor timing)
  • ⚠️ Selling at bottoms (poor exits)
  • ⚠️ Too many rapid trades (overtrading)

7. Trade History​

Detailed log of every trade executed during the backtest.

Trade Table Columns​

Type

  • 🟢 BUY: Long entry
  • 🔴 SELL: Position exit

Entry Date

  • When the position was opened
  • Format: YYYY-MM-DD

Exit Date

  • When the position was closed
  • Shows - if still open

Entry Value

  • Total capital committed
  • Includes fees

Exit Value

  • Total capital returned
  • Includes fees

Entry Price

  • Asset price when bought
  • In quote currency

Exit Price

  • Asset price when sold
  • In quote currency

Exit Reason

  • Signal: Normal strategy sell signal
  • Stop Loss: Hit stop loss threshold
  • Take Profit: Hit take profit target
  • End of Backtest: Position still open

P&L (Profit & Loss)

  • Net profit or loss for this trade
  • Green for profit, red for loss
  • Includes all fees

Status

  • Closed: Trade completed
  • Open: Position still active

Example Trade​

Type: BUY
Entry Date: 2024-01-15
Exit Date: 2024-01-20
Entry Value: 10,000.00 USDT
Exit Value: 10,500.00 USDT
Entry Price: 50,000.00 USDT
Exit Price: 52,000.00 USDT
Exit Reason: Signal
P&L: +500.00 USDT
Status: Closed

Analysis:

  • Bought BTC at $50,000
  • Sold at $52,000 (4% price increase)
  • After fees: $500 profit (5% return on this trade)
  • Held for 5 days

Interpreting Combined Metrics​

Scenario 1: High Win Rate, Low Returns​

Win Rate: 80%
Total Return: 5%
Profit Factor: 1.1

Diagnosis: Many small wins, but occasional large losses wipe them out. Solution: Tighten stop losses or increase take profit targets.

Scenario 2: Low Win Rate, High Returns​

Win Rate: 35%
Total Return: 45%
Profit Factor: 2.5

Diagnosis: Trend-following strategy - few but large wins. Solution: This is actually healthy; ensure you can handle the losing streaks psychologically.

Scenario 3: High Volatility, High Sharpe​

Volatility: 35%
Sharpe Ratio: 2.8
Total Return: 120%

Diagnosis: High-risk, high-reward strategy with good risk-adjusted returns. Solution: Consider position sizing to reduce volatility while keeping Sharpe high.

Scenario 4: Outperformed HODL, High Drawdown​

vs HODL: +15%
Max Drawdown: 40%

Diagnosis: Better returns than holding, but with significant pain along the way. Solution: Assess if the extra return justifies the emotional stress.

What Makes a Good Backtest Result?​

Look for these characteristics:

✅ Consistent Performance

  • Regular profits across different market conditions
  • No single trade makes or breaks the strategy

✅ Controlled Risk

  • Max drawdown under 25%
  • Sharpe ratio above 1.5
  • No catastrophic losses

✅ Profitable Economics

  • Profit factor above 1.5
  • Positive total and annual returns
  • Average win larger than average loss

✅ Reasonable Activity

  • Not overtrading (excessive fees)
  • Not undertrading (missing opportunities)
  • Win rate between 40-70%

✅ HODL Comparison

  • Ideally outperforms buy-and-hold
  • If underperforms, has significantly lower drawdown

Red Flags​

⚠️ Optimization Overfitting

  • Too perfect: 90%+ win rate, minimal drawdown
  • Likely issue: Won't work on new data

⚠️ Single Trade Dependency

  • One huge win accounts for all profits
  • Removing best trade makes strategy unprofitable

⚠️ Recent Performance Only

  • Works great last 3 months, terrible before that
  • May just be luck or recent market regime

⚠️ Excessive Drawdown

  • 50%+ max drawdown
  • Most traders can't handle this psychologically

⚠️ Low Sample Size

  • Only 5-10 trades in backtest
  • Not statistically significant

Next Steps After Reviewing Results​

If Results are Promising ✅​

  1. Test Different Time Periods: Validate on other date ranges
  2. Test Different Pairs: See if it works on multiple markets
  3. Optimize Parameters: Fine-tune (but avoid overfitting)
  4. Paper Trade: Test in real-time without risk
  5. Start Small: Deploy with minimal capital

If Results are Poor ❌​

  1. Adjust Parameters: Try different indicator settings
  2. Add Filters: Add trend or volatility filters
  3. Change Timeframe: Maybe your strategy works better on different timeframes
  4. Review Strategy Logic: Fundamental approach might be flawed
  5. Start Over: Sometimes a completely different strategy is needed

Example: Full Results Interpretation​

Strategy: RSI Mean Reversion
Market: BTC/USDT
Timeframe: 4h
Period: 365 days

Performance:
├─ Total Return: +45.2%
├─ Annual Return: +45.2%
├─ vs HODL: +12.3% (outperformed)
└─ Final Value: 14,520 USDT (from 10,000)

Risk Metrics:
├─ Win Rate: 62%
├─ Max Drawdown: 18.5%
├─ Sharpe Ratio: 2.1
├─ Profit Factor: 1.8
└─ Volatility: 15.3%

Trading Stats:
├─ Total Trades: 48
├─ Winning: 30
├─ Losing: 18
├─ Avg Win: 215 USDT
├─ Avg Loss: 95 USDT
├─ Largest Win: 890 USDT
└─ Largest Loss: 310 USDT

Analysis: ✅ Good win rate (62%) with favorable win/loss ratio (2.26:1) ✅ Excellent Sharpe ratio (2.1) - strong risk-adjusted returns ✅ Controlled drawdown (18.5%) - manageable risk ✅ Outperformed HODL by significant margin ✅ Reasonable trade count - not overtrading ✅ Profit factor 1.8 shows consistent edge

Decision: This looks like a solid strategy worth paper trading and potentially deploying with real capital.

Glossary​

Base Asset: The first currency in a pair (e.g., BTC in BTC/USDT) Quote Asset: The second currency in a pair (e.g., USDT in BTC/USDT) Drawdown: Decline from peak to trough HODL: "Hold On for Dear Life" - buy and hold strategy P&L: Profit and Loss Slippage: Difference between expected and actual execution price

Understanding these metrics empowers you to make informed decisions about strategy deployment and optimization.