Performance Review for Global Algorithmic Trading Software (GATS15) Strategy 3: Global Rapid Trend Catcher as of June 24, 2024

Performance Review for Global Algorithmic Trading Software (GATS15) Strategy 3: Global Rapid Trend Catcher as of June 24, 2024

Introduction

The Global Rapid Trend Catcher, as part of the Global Algorithmic Trading Software (GATS), is designed for the M15 timeframe. This strategy aims to capitalize on quick market trends, providing traders the ability to efficiently enter and exit positions based on short-term market momentum. Here are some detailed discussion points regarding this strategy:

Summary

  • Deposit/Withdrawal: $522,000,878.00
  • Credit Facility: $0.00
  • Closed Trade P/L: $487,081.84
  • Floating P/L: -$293,761.48
  • Margin: $2,064,529.07
  • Balance: $522,487,959.84
  • Equity: $522,194,198.36
  • Free Margin: $520,129,669.29

Detailed Performance Metrics

  1. Profitability:
    • Gross Profit: $1,707,609.73
    • Gross Loss: $1,220,527.89
    • Total Net Profit: $487,081.84
    • Profit Factor: 1.40
    • Expected Payoff: $1082.40
  2. Drawdown:
    • Absolute Drawdown: $1,021.87
    • Maximal Drawdown: $442,516.85 (54.91%)
    • Relative Drawdown: 56.22% ($170,107.99)
  3. Trading Statistics:
    • Total Trades: 450
    • Short Positions (won %): 156 (66.03%)
    • Long Positions (won %): 294 (72.45%)
    • Profit Trades (% of total): 316 (70.22%)
    • Loss Trades (% of total): 134 (29.78%)
  4. Trade Analysis:
    • Largest profit trade: $116,231.66
    • Largest loss trade: -$122,719.56
    • Average profit trade: $5,403.83
    • Average loss trade: -$9,108.42
  5. Consecutive Wins and Losses:
    • Maximum consecutive wins (count): 42 ($56,690.55)
    • Maximum consecutive losses (count): 21 (-$93,517.32)
    • Maximal consecutive profit (count): $671,421.75 (22)
    • Maximal consecutive loss (count): -$326,982.20 (6)
    • Average consecutive wins: 7
    • Average consecutive losses: 3

Key Strategies and Indicators

  1. Global Standard Initial Stop Loss (GSISL):
    • Using a 200-period ATR multiplied by 22 to manage rollover spread risks.
    • Manual adjustments during trade progression for stop-loss refinements.
  2. Indicators Used:
    • Exponential Moving Averages (EMA): EMA 25, EMA 50, EMA 100, EMA 200 for trend identification.
    • MACD (8, 13, 5): For momentum and potential entry points.
    • ADX: To determine trend strength.
    • RSI: For overbought or oversold conditions.
    • Stochastic Oscillator (14, 3, 3): For potential reversal points.
    • ATR: For setting stop-loss levels.

Performance Insights

  • Trend Capture Efficiency: The strategy’s ability to identify and ride significant trends is evident from the profit factor and the average profit per trade. The strategy capitalized on large price movements, leading to substantial gains, as seen in the largest profit trade of $116,231.66.
  • Risk Management: The use of a substantial ATR multiplier for stop loss has minimized the impact of volatile market movements, as seen in the controlled drawdown figures. The maximum drawdown of 54.91% highlights the strategy’s ability to handle market volatility while maintaining overall profitability.
  • Consistency: The high percentage of winning trades (70.22%) indicates the strategy’s robustness and reliability. With an average of 7 consecutive winning trades, the strategy demonstrates its ability to consistently capture profitable opportunities.

Recommendations for Improvement

  1. Stop-Loss Adjustments: Further refinement of the GSISL can be explored, possibly reducing the multiplier slightly to tighten risk management without compromising profit potential. By analyzing past trades, adjustments can be made to optimize the balance between risk and reward.
  2. Trade Frequency: The strategy could benefit from reducing the frequency of trades by filtering out lower probability setups, focusing on higher quality signals. This can be achieved by incorporating additional confirmation indicators or improving the existing ones.
  3. Additional Indicators: Incorporating additional trend confirmation indicators or improving existing ones to enhance signal accuracy and reduce false positives. For example, adding a volume-based indicator could provide further confirmation of trend strength.

About the Author

Dr. Glen Brown is the President & CEO of Global Accountancy Institute, Inc., and Global Financial Engineering, Inc. With a Ph.D. in Investments and Finance and over 25 years of experience, Dr. Brown is a renowned expert in financial markets, trading strategies, and risk management. His innovative approach to trading and education has empowered countless traders and investors worldwide.

General Disclaimer

Trading financial markets involves risk, and it is important to understand these risks before engaging in trading activities. This strategy and its content are for educational purposes only and do not constitute financial advice. Past performance is not indicative of future results.

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