Backtesting vs Reality:
In summary there are a staggering array of factors that can be simulated in order to generate a realistic backtest. The dangers of overfitting, poor data cleansing, incorrect handling of transaction costs, market regime change and trading constraints often lead to a backtest performance that differs substantially from a live strategy deployment.
Thus one must be very aware that future performance is very unlikely to match historical performance directly. We will discuss these issues in further detail when we come to implement an event-driven backtesting engine near the end of the book.
| Aspect | Backtesting | Live Trading |
|---|---|---|
| Data Type | Uses historical data | Uses real-time market data |
| Execution Speed | Instantaneous (simulated) | Depends on market conditions and broker execution |
| Slippage | No slippage | Slippage may occur due to market volatility |
| Spread | Uses fixed spread or historical spread data | Actual spread varies dynamically |
| Latency | Zero latency (simulated) | Subject to network and broker latency |
| Order Execution | Assumed perfect execution | May experience delays, rejections, or partial fills |
| Market Impact | No market impact | Large orders can affect market prices |
| Emotions | No emotional influence | Traders may be affected by fear and greed |
| Liquidity | Assumed infinite liquidity | Actual liquidity depends on market conditions |
| Commission & Fees | Often ignored or estimated | Actual broker commissions and fees apply |
| Slippage Control | No real slippage control | Needs robust risk management |
| Trading Costs | Theoretical costs used | Real costs fluctuate with the market |
| Execution Errors | No execution errors | Potential for broker/platform errors |
| Order Types | All orders assumed to execute correctly | Some orders may be rejected |
| Market Gaps | Rarely accounted for | Gaps can cause unexpected losses |
| Strategy Optimization | Easy to optimize with historical data | Harder to optimize in real-time |
| Overfitting Risk | High risk due to curve fitting | Less likely but still possible |
| Strategy Adaptability | Based on past data, not real-time adaptation | Must adapt to changing market conditions |
| Random Events | Cannot simulate news events effectively | News events can impact trades |
| Psychological Impact | No stress or decision fatigue | Trader psychology plays a major role |
| Market Depth | Limited or estimated | Real-time order book depth available |
| Connection Issues | No internet or server failure impact | Internet disconnections can cause issues |
| Broker Limitations | No broker intervention | Broker restrictions may apply |
| Requotes | No requotes | Possible requotes from brokers |
| Timeframe Accuracy | Can analyze long-term performance | Harder to test across long periods |
| Testing Flexibility | Can test multiple strategies in a short time | Live testing is slow and expensive |
| Speed of Results | Immediate insights | Requires real-time patience |
| Capital at Risk | No real money involved | Real capital is at risk |
| Confidence Building | Helps understand strategy performance | True test of confidence and discipline |
| External Factors | Ignores real-world events | Real-world factors affect performance |
| Regulatory Factors | No regulatory impact | Compliance and regulations may affect trading |
| Technical Issues | No platform crashes | Platform or broker outages can disrupt trading |
| Emotional Bias | No emotions involved | Fear and greed can impact decision-making |
| Portfolio Diversification | Can test multiple assets at once | Limited by capital and risk management |
| Learning Curve | Easier to learn and test | Harder due to real-world complexities |
Backtesting is the process of assessing a trading strategy’s efficacy by utilizing historical market data. Before risking real money, it assists traders in identifying vulnerabilities, optimizing parameters, and analyzing prospective profitability. Backtesting presupposes flawless trade execution, stable spreads, and minimal market impact because it runs in a simulated environment. Overfitting and irrational expectations may result from its inability to accurately simulate real-time circumstances like slippage, order rejections, or emotional decision-making.
Conversely, Live Trading entails making deals in real time when the market is actually functioning. It takes into consideration slippage, execution delays, actual spreads, and outside variables like news events and changes in liquidity. In contrast to backtesting, live trading subjects traders to emotional stressors like greed and fear, which may influence their choices. Live trading has financial risks and necessitates strong risk management to minimize losses, even though it yields more precise outcomes. A trading strategy can be efficiently improved and validated by combining the two methods.
Read Also; Transaction Cost In Algo Trading
