I Found 5 Strategies Nobody's Talking About (514%)

Summary

In this video, the speaker discusses a series of backtested trading strategies, highlighting impressive returns like 20,000% and 3777%. The strategies involve liquidation events, volume confirmation, and time-based scaling. He emphasizes key metrics such as profit factor, expectancy, and drawdown. While some strategies show excellent returns, others reveal concerning drawdowns. Throughout, the speaker shares code for viewers to access and iterates on different strategies. There's also a mix of humor and banter aimed at quants, underscoring the importance of having fun with finance while optimizing trading systems.

Takeaways

• 😀 The speaker discusses finding a 'holy grail' trading strategy with various high returns, such as 20,000%, 3777%, and 615% returns, and compares them to a 33% return from buy-and-hold strategies.
• 😀 Liquidation strategies are emphasized, with the speaker noting a 98% return and a maximum drawdown of 25% as a relatively solid result compared to other tests with larger drawdowns.
• 😀 Several backtest results show significant returns (e.g., 114% and 514%) but are balanced with varying drawdowns, such as 27% and 28%, pointing out that drawdown management is crucial for successful strategies.
• 😀 A time-based scaling strategy is introduced, where buying is triggered at specific hourly intervals after a liquidation event, aiming to optimize trade execution over time.
• 😀 The speaker mentions testing multiple trading bots and strategies in a 'cloud code' environment, showcasing the iterative nature of algorithmic trading.
• 😀 The speaker brings up the importance of code accessibility, sharing backtest results, and GitHub for viewers who are part of the 'Quant Elite' bootcamp or lifetime members.
• 😀 Emphasis is placed on risk management, with the speaker discussing drawdowns (e.g., 77% vs. 149%) and highlighting the balance between returns and how much risk (drawdown) a trader can tolerate.
• 😀 The speaker uses humor and energy to keep the audience engaged, even during the more technical aspects of the discussion, such as backtest code sharing and strategy analysis.
• 😀 The 'overfitting' of certain strategies is acknowledged, with caution advised against strategies that promise excessively high returns, such as the 20,000% return example.
• 😀 Throughout the video, the speaker emphasizes the importance of continuous iteration and testing in developing trading strategies, noting that no strategy is perfect, but constant refinement is key.

Q & A

What is the significance of the 20,000% return mentioned in the video?

The 20,000% return is highlighted as an exceptional result, but the speaker expresses doubt about its validity, suggesting it may be overfitted. This means the strategy might be too fine-tuned to historical data, potentially making it unrealistic in real-world conditions.

What does 'overfitting' mean in the context of the trading strategy?

Overfitting occurs when a strategy is excessively optimized for historical data, which can lead to unrealistically high returns. This often results in poor performance in live trading since the strategy is too tailored to past data and doesn't generalize well to new, unseen market conditions.

What is the main concern regarding strategies with high drawdowns?

The concern with high drawdowns is that they represent large losses during trading, which can be difficult for traders to endure psychologically. A large drawdown may lead to significant capital loss, and many traders may not have the emotional resilience to stick with a strategy that involves such big drawdowns.

What performance metrics are commonly used to evaluate a trading strategy in the video?

The key performance metrics mentioned include return on investment (ROI), maximum drawdown, Sharpe ratio, Sortino ratio, expectancy, and win rate. These metrics help assess the risk-reward balance and overall viability of the strategy.

Why is drawdown an important factor in evaluating trading strategies?

Drawdown is important because it reflects the worst peak-to-trough loss a strategy might experience. A large drawdown can be psychologically difficult for traders to handle, and excessive drawdowns can lead to account depletion. Therefore, traders often prefer strategies with smaller, more manageable drawdowns.

How does the speaker contrast different strategies based on their risk levels?

The speaker contrasts strategies by comparing their returns with their drawdowns. For example, one strategy with a high return of 149% also has a severe 71% drawdown, while another with a 77% return only has a 25% drawdown. This comparison raises the question of how much risk a trader is willing to tolerate for a given reward.

What role does cloud computing play in the backtesting of trading strategies?

Cloud computing is used for running trading algorithms and backtesting on large datasets. It allows the speaker to test multiple strategies and make use of cloud-based resources for faster and more efficient computation, enabling them to optimize and iterate on their trading strategies.

What is the 'time-based scaling' method mentioned in the video?

Time-based scaling refers to a strategy where trades are entered at specific intervals (e.g., every 1, 2, 3, or 4 hours) after a trigger is hit. The goal is to space out the entries over time, rather than entering all at once, to manage risk and adjust to market conditions gradually.

What is the speaker's opinion on the quant trading community, and how does he engage with it?

The speaker expresses a playful and somewhat critical view of the quant trading community, referring to them as 'quants' who can be arrogant. He enjoys 'rage baiting' them on social media by posting extreme performance results to provoke reactions and stir up debates.

How does the speaker recommend approaching the presented trading strategies for beginners?

For beginners, the speaker advises caution, particularly when evaluating strategies with high returns but equally high drawdowns. He suggests that beginners should be more focused on finding strategies with lower, more manageable drawdowns to reduce risk. He also emphasizes the importance of testing and iterating strategies before committing substantial capital.