Understanding Backtesting

At its core, backtesting involves applying a trading strategy to historical market data to determine its potential profitability and risk. This retrospective analysis helps traders identify strengths and weaknesses in their approach before risking actual capital. While backtesting cannot guarantee future success due to the ever-changing nature of markets, a strategy that has historically performed well may have a higher probability of future profitability.

Methods of Backtesting

1. Manual Backtesting: This approach requires traders to manually review historical charts, identify trade setups based on their strategy, and record the outcomes. Although time-consuming, manual backtesting allows traders to immerse themselves in market patterns, fostering a deeper understanding and trust in their strategy. It's crucial during this process to remain objective and avoid "curve-fitting," where one might unconsciously manipulate the strategy to fit historical data.​
   
   
2. Automated Backtesting: Utilizing programming languages like Python, MQL, or C++, traders can automate the backtesting process. This method enables the rapid testing of strategies across extensive datasets, eliminating emotional biases. However, it requires technical proficiency and careful attention to avoid overfitting the strategy to past data.

Key Variables to Consider 

When running a backtest, tracking key statistics helps traders evaluate the effectiveness of a strategy. Each metric provides insights into different aspects of risk, reward, and overall performance:

1. Time and date of entry: The exact time and date when a trade is entered. 

Why? Helps traders identify patterns in trade timing, assess market conditions at entry, and refine their strategy based on historical timing trends.

2. Entry and exit price: The price at which a trade is opened and closed.
Why? Essential for calculating profit/loss, risk-reward ratio, and understanding trade efficiency.

3. Position size and % risk on account: The number of units/contracts traded and the percentage of account capital risked per trade.
Why? Helps manage risk exposure, ensuring a strategy does not lead to excessive drawdowns.

4. Maximal adverse excursion (MAE): The largest unrealized loss experienced during a trade before it is closed.
Why? Indicates how much price moved against the position before it became profitable or was stopped out, helping traders refine stop-loss placement.

5. Maximal favorable excursion (MFE): The largest unrealized gain a trade reached before closing.
Why? Helps evaluate whether profit targets are optimized or if traders are exiting too early.

6. Average risk-reward ratio (RRR): The average ratio of potential profit to potential loss across all trades.
Why? A higher RRR means profitable trades tend to make more than what is lost on losing trades, an essential component of long-term profitability.

7. Win rate: The percentage of trades that resulted in a profit.
Why? A high win rate alone does not guarantee profitability, but combined with a good risk-reward ratio, it determines the strategy’s effectiveness.

8. Maximum drawdown: The largest peak-to-trough decline in account balance during the backtest period.
Why? Measures risk by showing the worst-case scenario of consecutive losses, helping traders assess strategy resilience.

9. Long/short ratio: The proportion of long (buy) trades versus short (sell) trades.
Why? Reveals strategy bias—whether it performs better in bullish or bearish markets.

10. Success rate on different instruments: The percentage of profitable trades for each asset class or instrument (stocks, forex pairs, commodities, etc.).
Why? Some strategies work better on specific instruments due to market conditions, volatility, or liquidity. Tracking this helps traders focus on assets where their strategy has a proven edge.

Ideal Backtesting Scenario: 

For backtesting to be truly useful, it must mirror real market conditions as closely as possible. A poorly designed backtest can create false confidence, making a strategy look profitable when it wouldn't actually hold up in live trading. To avoid this, traders need to consider a few key factors when running a backtest.

Use a realistic timeframe

• A good backtest covers a long enough period to include different market conditions - bullish, bearish, high volatility, and low volatility phases.
• Testing only recent data or a narrow window can skew results and fail to show how the strategy performs over time.

Include a variety of assets

• A backtest should include stocks or assets that performed well and those that failed (like companies that went bankrupt or got delisted).
• Using only surviving stocks leads to survivorship bias, making a strategy appear more successful than it actually is.

Factor in trading  costs

• Even small fees add up - spreads, commissions, and slippage can significantly impact actual returns.
• Many traders overlook these costs in backtesting, leading to inflated performance expectations.

Validate results with forward testing

• After backtesting, strategies should be tested on new, unseen data (out-of-sample testing) to confirm they work beyond the historical data set.
• Forward testing (paper trading) allows traders to test strategies in real market conditions without risking real money.
• If backtesting, out-of-sample testing, and forward testing show similar results, the strategy is more likely to work in live trading.

Key Takeaways 

• Backtesting is essential for evaluating a trading strategy's effectiveness. It helps traders assess potential profitability and risk before using real capital.
• Manual backtesting provides deep market insight, while automated backtesting enables faster and large-scale testing but requires technical skills.
• Tracking key backtesting statistics is crucial. Metrics like win rate, risk-reward ratio, maximum drawdown, and success rate across instruments help refine and optimize a strategy.
• A realistic backtesting setup ensures reliable results. Testing across different market conditions, using a diverse data set, and factoring in transaction costs prevent misleading conclusions.
• Forward testing is necessary for validation. After backtesting, out-of-sample testing and paper trading confirm whether a strategy holds up in real-time market conditions.