Summary of 3 ERRORES por los que tus resultados en BACKTESTING no son REALES

00:00:00 - 00:15:00

In this YouTube video titled "3 ERRORES por los que tus resultados en BACKTESTING no son REALES," the speaker compares the concept of trading to the butterfly effect in the movie "Back to the Future," where small actions in the past create alternate timelines. They emphasize the importance of understanding probabilities in trading and explain that simply winning for a month or two doesn't guarantee long-term success. The speaker warns against relying on free or purchased trading systems without conducting thorough backtesting. They explain that backtesting should involve a large sample size of trades to validate the profitability of a system and prevent sampling bias. The speaker also discusses the importance of understanding statistical analysis in backtesting, the danger of drawing conclusions based on small sample sizes, and the importance of collecting a large sample size and considering probabilities to ensure that the system is truly profitable in the long term. Additionally, they highlight three common mistakes that traders make when conducting backtesting, including cherry-picking results, not maintaining consistency throughout the testing period, and not having a sufficient sample size. The speaker emphasizes that while backtesting can be a useful tool, it should not be the sole basis for making trading decisions and also discusses three common mistakes that can lead to unreliable results in backtesting, such as overfitting the data, not considering transaction costs, and neglecting to account for market conditions and dynamics.

• 00:00:00 In this section, the speaker compares the concept of trading to the butterfly effect in the movie "Back to the Future," where small actions in the past create alternate timelines. They emphasize the importance of understanding probabilities in trading and explain that simply winning for a month or two doesn't guarantee long-term success. The speaker also warns against relying on free or purchased trading systems without conducting thorough backtesting. They explain that backtesting should involve a large sample size of trades to validate the profitability of a system and prevent sampling bias.
• 00:05:00 In this section, the speaker discusses the importance of understanding statistical analysis in backtesting. They explain that statistics is the science of approximation, and it is crucial to have a large enough sample size to reduce the margin of error in the system's percentage. They illustrate this concept with examples, such as the similarity between flipping a coin and trading. They also highlight the danger of drawing conclusions based on small sample sizes, showing how combining different strategies into one system can create different results depending on the timing of the backtesting. The speaker emphasizes the need to avoid bias and self-sabotage in the trading process, where traders tend to manipulate or discard data to fit their expectations.
• 00:10:00 In this section, the speaker discusses three common mistakes that traders make when conducting backtesting. The first mistake is cherry-picking results, where traders only keep the trades that align with their expectations and discard the losing ones. This creates a false sense of success in the backtesting results, which often leads to disappointment when the same system is applied in real trading. The second mistake is not maintaining consistency throughout the testing period. Traders sometimes deviate from the original set of rules and add new criteria based on past performance, creating a parallel universe of results that are no longer accurate. The third mistake is not having a sufficient sample size for the backtesting. Many traders rely on a small number of trades to determine the profitability of a system, which can lead to inaccurate conclusions. The speaker emphasizes the importance of collecting a large sample size and considering probabilities to ensure that the system is truly profitable in the long term.
• 00:15:00 In this section of the video, the speaker discusses three common mistakes that can lead to unreliable results in backtesting. These mistakes include overfitting the data, not considering transaction costs, and neglecting to account for market conditions and dynamics. Overfitting occurs when the trading strategy is overly tailored to historical data, resulting in poor performance in real-world scenarios. Transaction costs, such as commissions and slippage, can significantly impact the profitability of a strategy but are often overlooked in backtesting. Lastly, failing to account for market conditions and dynamics can lead to unrealistic performance expectations. It is important to remember that while backtesting can be a useful tool, it should not be the sole basis for making trading decisions.