V6 | Strategy Optimization

Summary

In this video, the trader reviews the results of a 35-trade backtest, focusing on optimization strategies for improving performance. Key points include analyzing risk-reward ratios, understanding the impact of consecutive losses, and identifying profitable setups based on time and market conditions. The trader discusses adjustments in risk management, including scaling up on certain days and focusing on high-probability opportunities like New York sessions. Insights on trade management, such as using break-even stops and partial exits, highlight the importance of refining strategies for better returns. The overall goal is to enhance consistency and profitability with well-defined rules.

Takeaways

• 😀 Optimizations based on backtesting led to significant improvements in trade performance, resulting in a total profit of 11k.
• 😀 A key adjustment in risk-reward ratio (RR) involved expanding the target to between 2 and 3.4R, allowing more flexibility and potentially higher profits.
• 😀 The risk management strategy revealed a maximum of 3 consecutive losses, prompting a need for mitigation strategies in such scenarios.
• 😀 The strategy performs better on long trades (63% win rate) compared to short trades (43% win rate), suggesting a focus on long opportunities.
• 😀 New York session is highly profitable with a 62% win rate, accounting for the majority of the profits, while London sessions are still profitable but less consistent.
• 😀 Thursdays emerged as the strongest trading day with a 71% win rate, suggesting a potential for higher risk and reward on this day.
• 😀 Mondays show lower performance, requiring reduced risk to minimize losses, especially given the lower win rates on this day.
• 😀 The analysis of consecutive losses indicated that after 3 consecutive losses, a winning trade is likely to follow, which could provide psychological comfort and guidance for trade management.
• 😀 Implementing partial exits or moving stops to break even after achieving around 1.6R is a strategy to preserve profits and reduce losses in certain setups.
• 😀 The trading strategy will focus on specific setups like internal liquidity, order block (OB) entries, and targeting trends to improve win rates, with a focus on the New York and London opens.

Q & A

What is the purpose of the 30 trade backtest mentioned in the script?

The purpose of the 30 trade backtest, which was extended to 35 trades, is to evaluate the performance of a trading strategy. The backtest provides data on profit, loss, win rates, and other key metrics to optimize the strategy moving forward.

Why is the Risk-Reward (RR) ratio set at 1.39, and what impact does break-even trades have on this value?

The Risk-Reward ratio is 1.39, partly due to the inclusion of break-even trades, which do not contribute to the profit or loss but can affect the overall ratio. Without these break-even trades, the target RR is set to 2.0, reflecting the intended risk-reward profile.

What insights were gained regarding trade targets, specifically with regard to 2R and 3.4R?

The analysis revealed that the ideal average trade target was 3.4R, and there might be value in aiming for trade targets between 2R and 3.4R. Scaling out at 2R and holding the rest to reach 3R could optimize the strategy, capturing more profit in favorable setups.

How did consecutive losses affect the strategy, and what adjustments are being considered to mitigate this?

The analysis shows a possibility of experiencing up to 3 consecutive losses, which can result in a 3% drawdown if risking 1% per trade. To manage this, the trader is considering developing rules to mitigate such losses or adjusting risk levels to accommodate consecutive losses.

What is the significance of the higher win rate on buy trades compared to sell trades?

The higher win rate on buy trades (63%) compared to sell trades (43%) suggests that the trader performs better in bullish market conditions. This insight leads to focusing more on long opportunities rather than short opportunities to enhance overall performance.

What role does the New York trading session play in the trader's strategy?

The New York session is the most profitable for the trader, with a 62% win rate, contributing significantly to the overall profit. This session is considered an A+ opportunity, and the trader plans to focus on it when looking for high-quality setups.

How does the trader plan to adjust risk management based on the days of the week?

The trader plans to adjust risk management by increasing risk from 1% on Mondays and 0-0.5% on Tuesdays (due to lower performance on those days), while Wednesdays to Fridays will see higher risk (2-2.5%) depending on the setup, with Thursdays being especially profitable.

What did the Monte Carlo simulation reveal about potential losses, and how will the trader use this information?

The Monte Carlo simulation revealed that a series of up to 10 consecutive losses could occur over 500 trades. Understanding this potential risk allows the trader to plan for risk mitigation strategies and prepare mentally for drawdowns when such scenarios arise.

How are filters and tags used in the analysis to improve the strategy?

Filters and tags help analyze specific conditions or setups, such as targeting internal liquidity or avoiding trades after certain draws have already been hit. By identifying high-performing setups using these tags, the trader can refine their strategy and focus on the most profitable trades.

What is the trader's final plan for optimizing their trading strategy moving forward?

The trader's optimized strategy focuses on entering trades during the New York and London opens, targeting internal liquidity, and ensuring that a clear trend and an order block are present. They will focus on long trades, use 5-10 assets, and adjust risk based on the day of the week and trade quality.