1. Objective and Scope

Objective:
The aim of this model is to quantitatively assess the performance of the Global Algorithmic Trading Software (GATS) Framework. The model evaluates profitability, risk management, trade activity, and additional volatility/risk-adjusted metrics. It then aggregates these into a composite score that reflects the overall effectiveness of the system.

Scope:
This model covers the following categories:

• Profitability Metrics
• Risk Management Metrics
• Trade Activity Metrics
• Additional Metrics (Volatility & Risk-Adjusted Returns)

2. Key Performance Metrics

A. Profitability Metrics

• Total Net Profit (NP): Total profit after deducting all losses.
• Gross Profit (GP) and Gross Loss (GL): Sum of profits from winning trades and losses from losing trades.
• Profit Factor (PF): PF=GP/GL
• Expected Payoff (EP) per Trade: Average profit per trade.
• Return on Investment (ROI): ROI=NP/(Starting Capital) ×100%

B. Risk Management Metrics

• Maximum Drawdown (MDD): Largest peak-to-trough decline.
• Recovery Factor (RF): RF=NP/MDD
• Sharpe Ratio (SR): Risk-adjusted return metric.
• Balance Drawdown (Absolute & Relative)
• Consecutive Wins and Losses

C. Trade Activity Metrics

• Total Number of Trades (T)
• Win Rate (WR): WR=Number of Winning Trades/T
• Average Profit per Trade (APT) and Average Loss per Trade (ALT)
• Largest Profit and Loss Trades

D. Additional Metrics

• Volatility Metrics (e.g., Average ATR)
• Risk-Adjusted Metrics (e.g., Sortino Ratio)
• Portfolio-Level Metrics (Beta, Correlation, etc.)

3. Weighted Scoring System

To combine these metrics into a composite performance score, we assign weights based on their relative importance:

• Profitability Score (PS): 40%
• Risk Management Score (RMS): 35%
• Trade Activity Score (TAS): 15%
• Additional/Volatility Metrics (AVM): 10%

Each metric within a category is normalized (scaled to a 0–1 range or as a percentile relative to historical/industry benchmarks). The composite score is then calculated as: Composite Score=0.40×PS+0.35×RMS+0.15×TAS+0.10×AVM

4. Demo Data and Calculations

A. Demo Data (Estimated for the Review Period)

Assume the following performance data for the GATS Framework over a review period (e.g., March 1–14, 2025):

• Profitability Metrics:
  • Total Net Profit (NP): $1,300
  • Gross Profit (GP): $2,020
  • Gross Loss (GL): -$720
  • Profit Factor (PF): 2.80
  • Expected Payoff (EP): $4.80 per trade
  • ROI: 14.44% (on a $9,000 starting capital)
• Risk Management Metrics:
  • Maximum Drawdown (MDD): $177 (2.06%)
  • Recovery Factor (RF): 7.34
  • Sharpe Ratio (SR): 0.26
  • Balance Drawdown (Relative): 2.06%
  • Average Consecutive Losses: 2 trades
• Trade Activity Metrics:
  • Total Trades (T): 271
  • Win Rate (WR): 81.92% overall (for long trades)
  • Average Profit Trade: $9.10
  • Average Loss Trade: -$14.73
  • Largest Profit Trade: $161
  • Largest Loss Trade: -$60.47
• Additional Metrics:
  • Average ATR: (Assume a normalized volatility score of 0.75 on our scale)
  • Risk-Adjusted Return Metric (e.g., Sortino Ratio): (Assume a normalized score of 0.60)

B. Normalization and Scoring

For simplicity, assume the following normalized scores (on a scale of 0 to 1) based on our data compared to industry benchmarks:

• Profitability Score (PS):
  • Net Profit/ROI/Profit Factor & Expected Payoff yield a composite normalized score of 0.85.
• Risk Management Score (RMS):
  • With a low drawdown, high recovery factor, but a lower Sharpe Ratio, assume a composite score of 0.70.
• Trade Activity Score (TAS):
  • High win rate and consistent trade performance provide a composite score of 0.80.
• Additional/Volatility Metrics (AVM):
  • Normalized volatility and risk-adjusted return metrics give a score of 0.65.

C. Calculating the Composite Score

Using the weights defined: Composite Score=0.40×0.85+0.35×0.70+0.15×0.80+0.10×0.65

Breaking it down:

• Profitability Contribution: 0.40×0.85=0.34
• Risk Management Contribution: 0.35×0.70=0.245
• Trade Activity Contribution: 0.15×0.80=0.12
• Additional Metrics Contribution: 0.10×0.65=0.065

Summing these: Composite Score=0.34+0.245+0.12+0.065=0.77

This composite score of 0.77 (on a scale of 0 to 1) indicates a strong overall performance relative to the defined benchmarks.

5. Model Implementation and Dashboard

Data Aggregation & Processing

• Data Collection:
  Gather historical and live data on profitability, risk, trade activity, and volatility metrics.
• Calculation & Normalization:
  Use programming languages (e.g., Python with Pandas/NumPy) or Excel to compute the raw metrics. Normalize them based on historical ranges or industry averages.
• Weighting and Aggregation:
  Apply the weights defined above to obtain sub-scores and calculate the overall composite score.

Dashboard Features

Develop an interactive dashboard with these features:

• Overall Composite Score: Visual gauge (e.g., a dial or scorecard).
• Category Sub-Scores: Separate sections for Profitability, Risk Management, Trade Activity, and Additional Metrics.
• Trend Visualization: Graphs/charts to display how each metric and the composite score evolve over time.
• Alerts/Thresholds: Set up notifications if any metric breaches predetermined thresholds (e.g., drawdown exceeds 3%, Sharpe ratio drops below 0.20).

Tools and Technologies

Potential tools for dashboard development include:

• Python: Libraries like Plotly, Dash, or Bokeh.
• Excel: For a simpler, spreadsheet-based solution.
• BI Tools: Tableau or Power BI for more advanced visualizations.

6. Continuous Improvement and Iteration

• Regular Updates: Update the model periodically with new trading data.
• Backtesting: Validate the model using historical data to refine weights and normalization methods.
• Sensitivity Analysis: Test how changes in individual metrics affect the composite score to ensure robustness.
• Feedback Loop: Use dashboard insights to adjust trading strategies and risk management protocols continuously.

Conclusion

The GATS Framework Performance Measurement Model provides a comprehensive, quantitative method to evaluate trading performance across multiple dimensions. By integrating profitability, risk management, trade activity, and additional volatility metrics into a weighted composite score, this model offers a clear and objective assessment of system effectiveness. Our demo data for the period March 1–14, 2025, illustrates how, with a starting capital of $9,000, a composite score of 0.77 reflects robust performance. This model not only validates the statistical edge of the GATS Framework but also provides actionable insights for continuous improvement.

About the Author

Dr. Glen Brown is a pioneer in financial engineering and algorithmic trading with decades of experience in bridging the gap between academic theory and real-world trading applications. As the visionary founder of Global Accountancy Institute, Inc. (GAI) and Global Financial Engineering, Inc. (GFE), Dr. Brown has developed innovative frameworks—including the proprietary Global Algorithmic Trading Software (GATS) framework—that dynamically adapt to market conditions and redefine risk management. His work continues to drive excellence and transformation in systematic trading, setting new benchmarks in the industry.

General Disclaimer

The information provided in this document is for educational and informational purposes only and should not be considered investment advice. Trading in financial markets involves risk, and past performance is not indicative of future results. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Global Accountancy Institute, Inc. (GAI) and Global Financial Engineering, Inc. (GFE) operate as closed proprietary firms. We do not offer any products or services to the general public, nor do we accept clients or external funds. All methodologies, including the GATS Framework, are exclusively developed and utilized internally as part of our proprietary trading systems.

Neither Dr. Glen Brown nor his affiliated institutions (GAI and GFE) accept any responsibility for any loss or damage incurred as a result of the use or application of the information provided.

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