Lesson 5: First Strategy Backtest
📚 Course Series: Complete Freqtrade Quantitative Trading Course
📖 Section: Part 2 - Backtesting Practice
⏱ Duration: 2 hours
🎯 Difficulty: ⭐⭐ Beginner
🎯 Learning Objectives
After completing this lesson, you will be able to:
- ✅ Understand what backtesting is and why we backtest
- ✅ Master basic backtesting command usage
- ✅ Read and understand various metrics in backtest reports
- ✅ Identify and resolve common backtesting errors
- ✅ Evaluate whether a strategy is worth further research
📋 Prerequisites
Required Conditions
- [x] Completed lessons 1-4
- [x] Freqtrade environment installed and configured
- [x] Downloaded at least 30 days of historical data
Environment Verification
# 1. Activate environment
conda activate freqtrade
# 2. Verify version
freqtrade --version
# Should show: freqtrade 2025.9 or higher
# 3. Verify strategy is available
freqtrade list-strategies -c config.json | grep Strategy001
# Should show: │ Strategy001 │ Strategy001.py │ OK │
# 4. Verify data is downloaded
freqtrade list-data -c config.json
# Should show downloaded trading pairs and timeframes
If any step has issues, please return to the corresponding lesson for review.
📖 Theoretical Knowledge
5.1 What is Backtesting?
Backtesting is the process of simulating trading strategies using historical data, aimed at evaluating the strategy's past performance.
How Backtesting Works
Historical Data → Strategy Logic → Simulated Trading → Performance Report
↓ ↓ ↓ ↓
Candle Data Buy/Sell Signals Position Records Profit Statistics
Example Explanation:
- Assume today is 2025-09-30
- We use historical data from 2025-09-01 to 2025-09-30
- Simulate what would happen if the strategy ran during these 30 days
- Statistics on profit/loss, win rate, maximum drawdown, etc.
Backtesting vs Live Trading Differences
| Dimension | Backtesting | Live Trading |
|---|---|---|
| Data | Historical data (known) | Real-time data (unknown) |
| Execution | Instant completion | Real waiting |
| Slippage | May be ignored | Actually exists |
| Fees | Estimated | Actually deducted |
| Psychology | No pressure | Emotional fluctuations |
⚠️ Important Note: Good backtesting doesn't guarantee good live trading, but poor backtesting definitely means poor live trading!
5.2 Why Backtest?
5 Key Reasons
-
Verify Strategy Logic
- Is the strategy concept correct?
- Are buy/sell signals reasonable?
-
Evaluate Profitability
- Can it make money? How much?
- What's the win rate?
-
Assess Risk Level
- What's the maximum drawdown?
- Can it be tolerated?
-
Optimize Parameters
- Which parameters work better?
- How to adjust stop-loss and take-profit?
-
Build Confidence
- Only use real money after strategy validation
Limitations of Backtesting
❌ What backtesting cannot do:
- Cannot predict the future (history doesn't exactly repeat)
- Cannot eliminate all risks
- Cannot replace live trading verification
✅ What backtesting can do:
- Quickly screen strategies
- Discover obvious problems
- Provide optimization directions
5.3 Basic Backtesting Process
graph LR
A[Prepare Data] --> B[Select Strategy]
B --> C[Set Parameters]
C --> D[Execute Backtest]
D --> E[Analyze Results]
E --> F{Strategy Good?}
F -->|Yes| G[Enter Live Testing]
F -->|No| H[Optimize or Abandon]
H --> C
💻 Practical Operations
5.4 First Backtest Command
Command Structure
freqtrade backtesting \
-c config.json \ # Configuration file
--strategy Strategy001 \ # Strategy name
--timerange 20250901-20250930 # Time range
Step-by-Step Execution
Step 1: Activate Environment
conda activate freqtrade
cd /path/to/freqtrade-strategies
Step 2: Verify Data
# Ensure data is downloaded
freqtrade list-data -c config.json
# Sample output:
# BTC/USDT 5m 2025-09-01 -> 2025-09-30
# ETH/USDT 5m 2025-09-01 -> 2025-09-30
Step 3: Execute Backtest
freqtrade backtesting \
-c config.json \
--strategy Strategy001 \
--timerange 20250901-20250930
Execution Process Explanation
After starting the backtest, you'll see output similar to this:
2025-09-30 16:00:00 - freqtrade - INFO - freqtrade 2025.9
2025-09-30 16:00:01 - freqtrade.configuration - INFO - Using config: config.json ...
2025-09-30 16:00:02 - freqtrade.exchange - INFO - Using Exchange "Binance"
2025-09-30 16:00:03 - freqtrade.optimize.backtesting - INFO - Loading data from 2025-09-01 to 2025-09-30
2025-09-30 16:00:04 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy Strategy001
Execution Time:
- 1 month data: about 5-30 seconds
- 3 months data: about 30-60 seconds
- 1 year data: about 2-5 minutes
5.5 Complete Backtest Report Interpretation
After backtesting completes, multiple tables will be displayed. Let's interpret them one by one:
📊 Table 1: BACKTESTING REPORT (Core Report)
BACKTESTING REPORT
┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ BTC/USDT │ 7 │ 0.52 │ 3.643 │ 0.36 │ 3 days, 20:05:00 │ 6 0 1 85.7 │
│ ETH/USDT │ 6 │ -0.98 │ -5.897 │ -0.59 │ 4 days, 5:28:00 │ 5 0 1 83.3 │
│ BNB/USDT │ 10 │ 0.92 │ 9.137 │ 0.91 │ 2 days, 7:17:00 │ 10 0 0 100 │
│ SOL/USDT │ 17 │ -0.29 │ -4.912 │ -0.49 │ 1 day, 3:23:00 │ 15 0 2 88.2 │
│ XRP/USDT │ 16 │ 0.21 │ 3.366 │ 0.34 │ 1 day, 9:40:00 │ 14 0 2 87.5 │
│ TOTAL │ 56 │ 0.1 │ 5.338 │ 0.53 │ 2 days, 2:11:00 │ 50 0 6 89.3 │
└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
Column-by-Column Explanation:
-
Pair (Trading Pair)
- Individual performance for each trading pair
- TOTAL row is the summary of all trading pairs
-
Trades (Number of Trades)
- Number of completed trades
- ⚠️ Too few (<10): Insufficient sample, unreliable
- ✅ Moderate (10-100): Reasonable
- ⚠️ Too many (>200): Possible overtrading
-
Avg Profit % (Average Return Rate)
- Average profit/loss percentage per trade
- Example: 0.52% means average profit of 0.52% per trade
- ✅ > 0.5%: Good
- ⚠️ < 0.1%: May lose money after fees
-
Tot Profit USDT (Total Profit USDT)
- Total profit/loss for the trading pair (unit: USDT)
- Example: BTC/USDT earned 3.643 USDT
-
Tot Profit % (Total Return Rate)
- Total return rate relative to initial capital
- Calculation: Total profit/loss / Initial capital
- Example: 0.36% means earned 3.6 USDT from 1000 USDT
-
Avg Duration (Average Position Time)
- Average holding time per trade
- Example: 3 days, 20:05:00 = 3 days 20 hours 5 minutes
- ⚠️ Too long: Low capital utilization, overnight risk
- ⚠️ Too short: High fee percentage
-
Win Draw Loss Win% (Win/Loss Statistics)
- Win: Number of profitable trades
- Draw: Number of breakeven trades
- Loss: Number of losing trades
- Win%: Win rate = Win / (Win + Loss)
- Example: 6 wins, 0 draws, 1 loss, 85.7% win rate
Key Observations:
- ✅ BNB/USDT performed best: 10 trades all wins, total return +0.91%
- ❌ ETH/USDT performed worst: Total return -0.59%
- ✅ Overall performance: 56 trades, 50 wins 6 losses, 89.3% win rate, total return +0.53%
📊 Table 2: LEFT OPEN TRADES REPORT (Open Positions Report)
LEFT OPEN TRADES REPORT
┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ ETH/USDT │ 1 │ 0.28 │ 0.277 │ 0.03 │ 7 days, 10:40:00 │ 1 0 0 100 │
│ BNB/USDT │ 1 │ 0.17 │ 0.167 │ 0.02 │ 7 days, 10:30:00 │ 1 0 0 100 │
│ XRP/USDT │ 1 │ -0.76 │ -0.762 │ -0.08 │ 4:10:00 │ 0 0 1 0 │
│ BTC/USDT │ 1 │ -2.33 │ -2.313 │ -0.23 │ 12 days, 21:25:00 │ 0 0 1 0 │
│ TOTAL │ 4 │ -0.66 │ -2.631 │ -0.26 │ 6 days, 23:41:00 │ 2 0 2 50.0 │
└──────────┴────────┴──────────────┴─────────────────┴──────────────┴───────────────────┴────────────────────────┘
Meaning:
- Trades still open when backtesting ended
- These trades are not included in the total profit above
- ⚠️ Note: Open trades may be losing (like BTC/USDT -2.33%)
Why are there open trades?
- Strategy is still waiting for sell signals
- Backtest time ended but trades aren't completed
- These trades would continue in live trading
How to evaluate?
- Few open positions (<5): Normal
- Overall loss on open positions: Need attention, strategy exit conditions may be too strict
📊 Table 3: ENTER TAG STATS (Entry Tag Statistics)
ENTER TAG STATS
┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ OTHER │ 56 │ 0.1 │ 5.338 │ 0.53 │ 2 days, 2:11:00 │ 50 0 6 89.3 │
│ TOTAL │ 56 │ 0.1 │ 5.338 │ 0.53 │ 2 days, 2:11:00 │ 50 0 6 89.3 │
└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
Meaning:
- Statistics classified by entry reason
- Strategy001 marks all entries as "OTHER" (default tag)
- Advanced strategies can set multiple entry tags, showing which entry method works better
Example (Advanced Strategy):
Enter Tag | Entries | Avg Profit %
-------------+---------+--------------
breakout | 20 | 1.2% ← Breakout entry performs well
oversold | 15 | -0.5% ← Oversold entry performs poorly
📊 Table 4: EXIT REASON STATS (Exit Reason Statistics)
EXIT REASON STATS
┏━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
┡━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ roi │ 48 │ 1.01 │ 48.639 │ 4.86 │ 1 day, 10:01:00 │ 48 0 0 100 │
│ force_exit │ 4 │ -0.66 │ -2.631 │ -0.26 │ 6 days, 23:41:00 │ 2 0 2 50.0 │
│ stop_loss │ 4 │ -10.18 │ -40.670 │ -4.07 │ 5 days, 6:40:00 │ 0 0 4 0 │
│ TOTAL │ 56 │ 0.1 │ 5.338 │ 0.53 │ 2 days, 2:11:00 │ 50 0 6 89.3 │
└─────────────┴───────┴──────────────┴─────────────────┴──────────────┴───────────────────┴────────────────────────┘
This is one of the most important tables! Tells you how trades ended.
Exit Reason Explanations:
-
roi (Take Profit)
- Automatically take profit when ROI target is reached
- Example: 48 trades took profit through ROI, average return +1.01%
- ✅ Excellent performance: All profitable
-
exit_signal (Signal Sell)
- Strategy generates sell signal
- Example: Not present in this backtest
- 📝 Strategy001 uses
exit_profit_only = True, only responds to sell signals when profitable
-
stop_loss (Stop Loss)
- Stop loss triggered
- Example: 4 stop losses, average loss -10.18%
- ⚠️ Warning: High stop loss ratio indicates strategy is risky
-
force_exit (Force Close)
- Open positions at backtest end are forcibly closed
- Example: 4 trades, average -0.66%
- 📝 These are the trades from the "Open Positions Report" above
Key Observations:
- ✅ Most trades (48/56 = 85.7%) took profit through ROI
- ⚠️ 4 stop losses caused -40.670 USDT in losses
- 📊 Without stop losses, total return would be +46 USDT (5.338 + 40.670)
📊 Table 5: SUMMARY METRICS (Summary Metrics)
SUMMARY METRICS
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Metric ┃ Value ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Backtesting from │ 2025-09-01 00:00:00 │
│ Backtesting to │ 2025-09-30 00:00:00 │
│ Trading Mode │ Spot │
│ Max open trades │ 5 │
│ │ │
│ Total/Daily Avg Trades │ 56 / 1.93 │
│ Starting balance │ 1000 USDT │
│ Final balance │ 1005.338 USDT │
│ Absolute profit │ 5.338 USDT │
│ Total profit % │ 0.53% │
│ CAGR % │ 6.93% │
│ Sortino │ 0.86 │
│ Sharpe │ 1.22 │
│ Calmar │ 9.71 │
│ SQN │ 0.24 │
│ Profit factor │ 1.12 │
│ Expectancy (Ratio) │ 0.10 (0.01) │
│ Avg. daily profit │ 0.184 USDT │
│ Avg. stake amount │ 99.739 USDT │
│ Total trade volume │ 11198.536 USDT │
│ │ │
│ Best Pair │ BNB/USDT 0.91% │
│ Worst Pair │ ETH/USDT -0.59% │
│ Best trade │ SOL/USDT 1.45% │
│ Worst trade │ SOL/USDT -10.18% │
│ Best day │ 7.981 USDT │
│ Worst day │ -29.492 USDT │
│ Days win/draw/lose │ 19 / 8 / 3 │
│ Min/Max/Avg. Duration Winners │ 0d 01:00 / 8d 02:35 / 1d 15:48 │
│ Min/Max/Avg. Duration Losers │ 0d 04:10 / 12d 21:25 / 5d 16:42 │
│ Max Consecutive Wins / Loss │ 40 / 3 │
│ Rejected Entry signals │ 0 │
│ Entry/Exit Timeouts │ 0 / 0 │
│ │ │
│ Min balance │ 1000.996 USDT │
│ Max balance │ 1040.2 USDT │
│ Max % of account underwater │ 3.62% │
│ Absolute drawdown │ 37.676 USDT (3.62%) │
│ Drawdown duration │ 4 days 07:15:00 │
│ Profit at drawdown start │ 40.2 USDT │
│ Profit at drawdown end │ 2.524 USDT │
│ Drawdown start │ 2025-09-21 03:50:00 │
│ Drawdown end │ 2025-09-25 11:05:00 │
│ Market change │ 6.66% │
└───────────────────────────────┴─────────────────────────────────┘
Core Metrics Detailed Explanation:
📈 Profit Metrics
-
Total profit %: Total return rate 0.53%
- 1000 USDT → 1005.338 USDT
-
CAGR %: Annualized return rate 6.93%
- If this return rate continues for a year, annualized return 6.93%
- ⚠️ Note: This is just 1 month of data annualized, doesn't represent real annualized return
-
Avg. daily profit: Average daily profit 0.184 USDT
- Average profit of 0.184 USDT per day
📊 Risk Metrics
-
Max % of account underwater (Maximum Drawdown): 3.62%
- Maximum decline from highest point to lowest point
- Example: Account highest 1040.2 USDT, lowest fell to 1002.524 USDT
- Calculation: (1040.2 - 1002.524) / 1040.2 = 3.62%
- ✅ < 10%: Controllable risk
- ⚠️ 10-20%: Moderate risk
- ❌ > 20%: High risk
-
Sharpe Ratio: 1.22
- Risk-adjusted return metric
- ✅ > 1.0: Good
- ✅ > 2.0: Excellent
- ❌ < 0: Loss
-
Sortino Ratio: 0.86
- Similar to Sharpe, but only considers downside risk
- ✅ > 1.0: Good
📉 Win/Loss Statistics
-
Win rate: 89.3% (50 wins / 56 trades)
- ✅ > 60%: Good
- ⚠️ < 50%: Need to improve profit/loss ratio
-
Profit factor: 1.12
- Total profit / Total loss
- Calculation: (48.639 + ...other profits) / (40.670 + ...other losses)
- ✅ > 1.5: Excellent
- ✅ > 1.0: Profitable
- ❌ < 1.0: Loss
-
Max Consecutive Wins / Loss: 40 consecutive wins / 3 consecutive losses
- Longest winning and losing streaks
- ⚠️ Long losing streaks can affect psychology
🔍 Other Key Metrics
-
Best trade / Worst trade: +1.45% / -10.18%
- Best and worst single trades
- Risk Warning: Worst trade -10.18% = strategy's stoploss
-
Days win/draw/lose: 19 profitable days / 8 breakeven / 3 losing days
- Daily profit/loss statistics
- 📊 19 out of 30 days were profitable
-
Market change: 6.66%
- Market increase during same period
- Comparison: Strategy earned 0.53%, market rose 6.66%
- ⚠️ Underperformed market! Simply holding BTC might have been better
5.6 How to Judge Strategy Quality?
Based on this backtest report, let's evaluate Strategy001:
✅ Advantages
- High win rate: 89.3%, very stable
- Small drawdown: 3.62%, controllable risk
- High take-profit efficiency: 85.7% of trades took profit through ROI
- Sharpe > 1: Good risk-adjusted returns
❌ Disadvantages
- Low returns: 0.53%, underperformed market (6.66%)
- Large stop loss: -10% stop loss causes large single losses
- Long holding time: Average 2 days, low capital utilization
- Small sample: Only 56 trades
🎯 Overall Evaluation
Rating: ⭐⭐⭐ / ⭐⭐⭐⭐⭐
Applicable Scenarios:
- ✅ Conservative investors
- ✅ Range-bound markets
- ❌ Bull markets (underperforms market)
- ❌ Pursuing high returns
Recommendations:
- Can try Dry-run verification
- Need to optimize stop-loss strategy
- Consider reducing holding time
- Not recommended in bull markets
🐛 Common Errors and Solutions
Error 1: No data found
ERROR - No data found. Terminating.
Causes:
- Timeframe mismatch (strategy uses 5m, but only 1h data downloaded)
- No data in time range
Solution:
# Check timeframe used by strategy
grep "timeframe" user_data/strategies/Strategy001.py
# Output: timeframe = '5m'
# Download corresponding data
freqtrade download-data -c config.json --days 30 --timeframes 5m
Error 2: Strategy not found
ERROR - Impossible to load Strategy 'Strategy001'
Causes:
- Strategy name spelling error
- Strategy file not in strategies directory
Solution:
# List all available strategies
freqtrade list-strategies -c config.json
# Confirm strategy name (case sensitive)
Error 3: Timeframe mismatch
ERROR - Timeframe '15m' is not available. Available: ['5m']
Causes:
- Backtest specified timeframe doesn't match downloaded data
Solution:
# Method 1: Download corresponding timeframe data
freqtrade download-data -c config.json --days 30 --timeframes 15m
# Method 2: Backtest using existing timeframe
freqtrade backtesting -c config.json --strategy Strategy001 --timeframe 5m
📝 Practical Tasks
Task 1: Basic Backtesting (Required)
Objective: Complete your first backtest
# 1. Backtest Strategy001 (2025-09-01 to 2025-09-30)
freqtrade backtesting \
-c config.json \
--strategy Strategy001 \
--timerange 20250901-20250930
# 2. Record the following metrics:
- Total trades: _______
- Win rate: _______
- Total return: _______
- Maximum drawdown: _______
- Sharpe Ratio: _______
Submission:
- [ ] Backtest report screenshot
- [ ] Key metrics record table
- [ ] Your strategy evaluation (100-200 words)
Task 2: Error Diagnosis (Optional)
Objective: Familiarize with common errors and solutions
Try executing the following "error" commands, observe error messages, and fix them:
# Error 1: Non-existent strategy
freqtrade backtesting -c config.json --strategy NotExistStrategy
# Error 2: Non-existent trading pair
# First edit config.json, add "FAKE/USDT" to pair_whitelist
# Error 3: Timeframe without downloaded data
freqtrade backtesting -c config.json --strategy Strategy001 --timeframe 1h
# For each error:
# 1. Record error message
# 2. Analyze cause
# 3. Write solution
Task 3: Comparative Analysis (Advanced)
Objective: Compare backtest results from different time ranges
# Backtest 1 month
freqtrade backtesting -c config.json --strategy Strategy001 --timerange 20250901-20250930
# Backtest 3 months (need to download data first)
freqtrade backtesting -c config.json --strategy Strategy001 --timerange 20250701-20250930
Create comparison table:
| Metric | 1 Month | 3 Months | Difference Analysis |
|---|---|---|---|
| Number of trades | |||
| Win rate | |||
| Total return | |||
| Maximum drawdown | |||
| Sharpe |
Thinking Questions:
- Are the 1-month and 3-month results very different?
- Which time range is more credible?
- If differences are large, what does that indicate?
📚 Extended Reading
Recommended Articles
Related Documentation
- 📄 TESTING_GUIDE.md - Complete Testing Guide
- 📄 STRATEGY_SELECTION_GUIDE.md - Strategy Selection Guide
🎓 Post-Class Quiz
Multiple Choice
-
What is the main purpose of backtesting?
- A. Predict future prices
- B. Evaluate strategy historical performance
- C. Guarantee live trading profits
- D. Replace live trading
-
What happens with a 90% win rate but 1:10 profit/loss ratio strategy?
- A. Big profits
- B. Big losses
- C. Breakeven
- D. Cannot determine
-
What does maximum drawdown of 30% mean?
- A. Maximum 30% loss per trade
- B. Fell 30% from highest to lowest point
- C. 70% win rate
- D. 30% total loss
-
What does Sharpe Ratio < 0 indicate?
- A. Strategy is profitable
- B. Strategy is losing
- C. Risk is too high
- D. Insufficient data
-
Why doesn't good backtesting guarantee good live trading?
- A. History doesn't repeat
- B. Slippage and psychological factors exist
- C. Possible overfitting
- D. All of the above
Calculation Problems
Initial capital 1000 USDT, final 1053 USDT, what is the total return rate?
10 trades, 7 wins 3 losses, what is the win rate?
Account rose from 1000 to 1200, then fell to 1100, what is the maximum drawdown?
Practical Problems
-
Please explain this backtest result:
- Number of trades: 200
- Win rate: 45%
- Average profit: +2%
- Average loss: -1%
- Is this strategy profitable or losing?
If a strategy earns 50% in bull markets and loses 30% in bear markets, would you use it? Why?
Answers at end ⬇️
✅ Learning Checklist
Before proceeding to the next lesson, ensure you have:
- [ ] Understood what backtesting is and its purpose
- [ ] Able to execute basic backtesting commands
- [ ] Able to read and understand the 5 tables in backtest reports
- [ ] Know how to judge strategy quality
- [ ] Able to identify and resolve common errors
- [ ] Completed at least 3 backtests with different strategies/time ranges
- [ ] Understood limitations of backtesting
If you have any questions, please return to the corresponding section for review, or ask in the community.
🎯 Next Lesson Preview
Lesson 6: Strategy Performance Analysis
In Lesson 6, we will:
- Deep dive into how to evaluate strategy quality
- Master multi-dimensional strategy comparison methods
- Learn to create professional strategy comparison tables
- Understand the importance and weighting of different metrics
💬 Post-Class Discussion
Thinking Questions
- What do you think constitutes a "good strategy"?
- If backtesting returns are high but drawdown is also large, would you use it?
- Backtesting 1 month vs backtesting 1 year, which is more important?
Share Your Backtest Results
Welcome to share in the community:
- Your first backtest results
- Problems encountered and solutions
- Evaluation of Strategy001
📌 Quiz Answers
- B
- B (9 small wins + 1 big loss = overall loss)
- B
- B
- D
- (1053 - 1000) / 1000 = 5.3%
- 7 / 10 = 70%
- (1200 - 1100) / 1200 = 8.33%
- Profitable. Expected return = 45% × (-1%) + 55% × 2% = 0.65% > 0
- No. Although it looks profitable, can't predict if future is bull or bear, too risky.
Happy learning! Ready for Lesson 6? 🚀
📝 Course Feedback: If any part of this lesson is unclear, feel free to provide suggestions!
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