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Grok Bot+18 agentsTrading Strategy Comparison Matrix
Compare trading strategies using normalized performance, risk, and execution metrics to find the best fit for your goals.
$9.99
Trading Strategy Comparison Matrix
Example session with this skill installed
Compare my 'London Breakout' (50 trades, 45% win, 2.5:1 RR, 8% max DD) with my 'Daily Mean Reversion' (20 trades, 65% win, 1.2:1 RR, 4% max DD). I have 2 hours daily for execution.
- Read your context and instructions
- Compiled the trading strategy comparison
I have normalized the data for your London Breakout and Daily Mean Reversion setups. The comparison focuses on expectancy, execution burden, and fit for your 2-hour daily window.
Strategy Comparison Matrix
| Strategy | Win Rate | Expectancy (R) | Max Drawdown | Frequency | Complexity | Risk Profile | Best Fit |
|---|---|---|---|---|---|---|---|
| London Breakout | 45% | 0.57 R | 8.0% | High | Medium | Aggressive | Active Sessions |
| Daily Mean Rev | 65% | 0.43 R | 4.0% | Low | Low | Conservative | Low Screen-Time |
Performance & Risk Analysis
London Breakout: Higher expectancy (0.57R) but requires navigating significantly higher drawdown (8%). The lower win rate (45%) suggests longer losing streaks, requiring higher psychological resilience.
*
Daily Mean Reversion: More consistent win rate (65%) and shallower drawdown (4%). While the expectancy is lower per trade (0.43R), the equity curve is likely smoother.
Execution & Opportunity
London Breakout: High execution difficulty for your schedule. London open usually requires intense focus during a specific window. 50 trades indicates a high-frequency setup that may conflict with a 2-hour limit.
*
Daily Mean Reversion: Best fit for your constraints. 20 trades suggests a selective, slower-paced approach. The "Low" complexity rating and daily timeframe suit a 2-hour daily routine.
Trade-Off Summary
Yield vs. Stability: The London Breakout offers higher potential returns but at the cost of double the drawdown risk.
2.
Frequency vs. Schedule: You are trading off the high volume of the Breakout strategy for the schedule-friendly nature of the Mean Reversion setup.
Next steps
*
Calculate Sample Error: The Daily Mean Reversion sample (20 trades) is small; continue logging to confirm the 65% win rate holds.
- Time Audit: Review if the London Breakout trades actually occur within your 2-hour availability window.
Risk Scaling: Consider if the London Breakout's 8% drawdown is acceptable for your account type (e.g., funded vs. personal).
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Traders often struggle to objectively compare different setups, leading to overtrading or choosing strategies that don't fit their risk tolerance. Comparing a high-frequency scalping method against a selective swing strategy without normalized metrics results in poor capital allocation.
What it does
- Normalizes raw journal data and backtest summaries into consistent metrics like expectancy and profit factor.
- Generates a comprehensive comparison matrix across performance, risk, opportunity, and execution dimensions.
- Identifies critical trade-offs, such as higher win rates versus larger drawdowns or increased complexity versus setup frequency.
- Maps strategies to specific trader profiles based on screen-time availability and drawdown tolerance.
- Updates existing matrices with new weekly or monthly journal data to track performance drift.
Why this beats prompting it yourself
Generic prompts often hallucinate performance stats or fixate on win rate while ignoring risk-adjusted returns. This skill enforces strict data normalization, flags small-sample bias, and prevents the "universal winner" fallacy by focusing on situational best-fits.
Use cases
- Comparing backtest results of a new Mean Reversion setup against an existing Trend Following strategy.
- Auditing a monthly trading journal to see which specific setups are yielding the highest expectancy.
- Selecting a strategy for a funded account challenge based on strict drawdown and frequency requirements.
- Evaluating the execution burden of a systematic strategy versus a discretionary approach for part-time trading.
Known limitations
Requires structured input data such as trade counts, win/loss ratios, or documented risk rules. Cannot generate comparison metrics if raw performance data is entirely missing.
Requires structured data (trade counts, win/loss, drawdown). Does not provide financial advice, market predictions, or trade signals. Effectiveness depends on the quality of user-supplied data.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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