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Scrab's backtesting tools let you test investment strategies using past data, showing how they might have performed historically. To learn more about how to set up a backtest, please visit 'How to set up Backtests.'

In this article we'll cover:

  • Avoiding Backtest Pitfalls: Understand common mistakes to steer clear of for more reliable backtesting results.
  • Effective Backtesting Steps: Discover the best way to backtest by focusing on your strategy's criteria, not just the stocks.
  • Understanding Survivorship Bias: Learn why considering all stocks, not just the current winners, makes for more accurate backtesting.
  • Setting Up Your Backtest: Tips on choosing how many stocks to test, their weight, and when to rebalance.
  • Interpreting Backtest Results: Reminds you that past success doesn't guarantee future returns and the importance of fundamental analysis.

Avoiding Backtest Pitfalls

Backtesting your stock selections by looking at their historical performance may lead to misleadingly positive results. Doing that often involves choosing stocks that are successful now, not necessarily those that would have been chosen in the past. Choosing to backtest Netflix wouldn't make sense; since it's based on hindsight (you already know the stock is successful), this process doesn't accurately reflect the effectiveness of an investment strategy. 

Although Scrab offers a Model Portfolio for straightforward portfolio comparisons, the more impactful tool is our Backtests, designed to offer a deeper, more accurate analysis of investment strategies over the years.

Effective Backtesting Steps

The effective method for backtesting involves assessing the criteria for stock selection (or strategy) rather than the stocks themselves, following three steps: 

1) Define your investing criteria, such as specific financial metrics. 

2) Use these criteria to build an automated scoring model for finding suitable stocks. 

3) Utilize the Backtests tool to identify stocks that met these criteria in the past and analyze the hypothetical performance of such a portfolio over the years. 

This approach simulates long-term investing behavior and evaluates the strategy's potential success without risking real money.

Understanding Survivorship Bias

Survivorship bias in backtesting means you're only looking at stocks that have survived, which can lead to overly optimistic results. Our tool counters this by analyzing the stock pool as it was at each point in the past, ensuring a more accurate simulation. 

The S&P 500's constant updating further complicates backtesting, as it swaps out weaker stocks for stronger ones each quarter. Our system's approach accounts for these changes, conducting rolling backtests that reflect the actual composition of the S&P 500 over time, providing you with reliable insights into your strategy's effectiveness.

Setting Up Your Backtest

In backtesting, adjusting the number of stocks, their weights, and rebalancing frequency can significantly impact investment outcomes. As there is no universal solution, exploring different strategies and stock selections is crucial to find what works best.

Practical wisdom suggests starting with about 25 stocks and monthly rebalancing. Yet, integrating risk-adjusted weighting schemes like Hierarchical Risk Parity might offer additional gains. Ultimately, only through backtesting with Scrab can you accurately evaluate and refine your strategy with concrete data, moving beyond theory to informed, data-driven decisions.

Interpreting Backtest Results:

While past backtest results offer insights, they don't assure future gains. Historical performance serves as a guide, not a guarantee. For accurate investment decisions, itโ€™s crucial to understand the reasons behind the data patterns and distinguish between correlation and causation. Investments should be based on solid, fundamental explanations, ensuring strategies are sensible and data-driven. 

Understanding the underlying causes behind correlations ensures that investment decisions are not just data-driven but fundamentally sound, maximizing the chances of future success. To learn more, see our article, 'Interpreting Backtest Results'.