You cannot find the perfect trading strategy
Every quantitative researcher or programmer aspires to discover a trading strategy that generates alpha across all conceivable timeframes while avoiding any negative returns. In mathematical terms, it represents the unattainable limit of a flawless upward trend. Another motivation behind this pursuit is a fundamental disagreement with the market efficiency hypothesis; we firmly contend that this hypothesis is flawed.
Guru traders and “experts”
Scanning through social media platforms, YouTube, and numerous blogs reveals an abundance of seemingly “perfect trading strategies.” Why is there such a proliferation of these strategies? Primarily, it serves the purpose of driving web traffic, generating revenue through advertising, or enticing individuals to purchase a “premium ultimate course.” You are the product.
The perfect strategy is not online
Returning to fundamental principles, a viable trading strategy seeks to achieve overperformance relative to a specific universe or index. Otherwise, opting for an ETF would be a more straightforward choice. The impact of employing such a strategy in the market is to outperform at least 50% of market participants in terms of flows. Consequently, if you choose to disclose your strategy online and it becomes widely adopted, you risk losing the arbitrage opportunity. The market is adaptive; it absorbs and nullifies inefficiency arbitrage effects. Therefore, if you stumble upon a valuable insight, exercise discretion in sharing it entirely, or ensure you secure the signal first.
Your backtesting is wrong
In the world of financial quantitative trading, the allure of backtesting is undeniable. The ability to simulate trading strategies on historical data provides traders with a powerful tool for refining and optimizing their approaches. However, the stark contrast between backtesting results and real-world performance has long been a challenge for quant traders. This article explores the nuances of backtesting and the importance of bridging the gap between simulated success and the unpredictable nature of live markets.
The Promise of Backtesting
Backtesting, in essence, is the process of applying a trading strategy to historical market data to evaluate its performance. Traders utilize this method to gauge how a strategy would have fared under past market conditions, providing valuable insights into potential profitability, risk management, and overall effectiveness.
The benefits of backtesting are numerous. It allows traders to optimize their strategies by tweaking parameters, assessing various time frames, and adapting to different market conditions—all within a controlled, simulated environment. Backtesting provides a level of precision and granularity that is hard to achieve in the dynamic and unpredictable real-world market.
The Pitfalls of Backtesting
While backtesting serves as a valuable tool for strategy development, it comes with inherent limitations that can lead traders astray when applied without due caution. One of the primary pitfalls lies in the assumption that past performance guarantees future results. Markets are dynamic and subject to constant change, making historical data only a partial reflection of the complexities faced by traders in the present.
Backtesting models often operate under certain assumptions, such as the availability of liquidity at all times, which may not hold true in reality. Slippage, transaction costs, and market impact are additional factors that are challenging to accurately replicate in a backtesting environment. These discrepancies can significantly impact the performance of a trading strategy when applied to live markets.
Closing the Gap
To address the disparities between backtesting and live trading, it is crucial for quant traders to adopt a more realistic approach. Here are some strategies to help bridge the gap:
1. Incorporate Real-world Variables:
Introduce transaction costs, slippage, and market impact into your backtesting model. This more accurately reflects the challenges faced in live markets and helps set realistic expectations for performance.
2. Regularly Update Models:
Markets evolve, and what worked in the past may not be as effective in the present. Regularly update and recalibrate your models to adapt to changing market conditions.
3. Validate Strategies in Real-time:
Implementing a strategy in a live, controlled environment with limited capital—commonly known as paper trading—can provide valuable insights into its behavior and performance in real-time conditions.
4. Exercise Prudent Risk Management:
Risk management is paramount in both backtesting and live trading. Implement robust risk controls and understand the limitations of your strategy to avoid catastrophic losses.
While backtesting is an invaluable tool for refining trading strategies, it is not a crystal ball that can predict the future with certainty. Traders must approach backtesting with a critical eye, recognizing its limitations and actively working to bridge the gap between simulation and reality. By embracing a more nuanced and realistic perspective, financial quant traders can navigate the complexities of live markets more effectively and increase the probability of success in their trading endeavors.
To conclude, please be cautious with any trading advice you find on public places. To be profitable in the long run using a specific method, you need to understand what you are doing exactly. The decisions are yours, not others