Episode 27: PEE Analytics – Exit Timing Analysis and Post-Exit Excursion Insights

Welcome to TheFinalTape Academy – Episode 27: PEE Analytics – Exit Timing Analysis and Post-Exit Excursion Insights

You have audited overall performance, implemented targeted fixes, and analyzed setups and long-term trends.

Now navigate to CharterElite Analytics in the sidebar, then select the PEE Analytics tab (often the first or second tab, marked with a flask or analytics icon).

This tool focuses specifically on what happens after you close a trade. It examines post-exit price movement to determine whether your exits were optimally timed, prematurely early, or appropriately protective — using objective data rather than subjective judgment.

Accessing PEE Analytics

1. Sidebar → CharterElite Analytics
2. Tab bar → PEE Analytics
3. Expand the tab’s help accordion at the top for a quick overview: “What it shows,” “Why it matters,” and “How to use.”

Core Concepts – Post-Exit Excursion Metrics

| Term | Definition | Interpretation | |---------------|----------------------------------------------------------------------------|--------------------------------------------------------------------------------| | MFPE | Maximum favorable price movement after exit (highest high on long, lowest low on short) | Indicates missed upside (“left on the table”) | | MAPE | Maximum adverse price movement after exit (lowest low on long, highest high on short) | Indicates avoided downside (“dodged additional loss”) | | MFPE-first| Favorable move occurred first after exit | Suggests early exit on winners or premature stop on losers | | MAPE-first| Adverse move occurred first after exit | Suggests stop was tested but price recovered (winners) or good protective cut (losers) | | R-multiple| MFPE and MAPE expressed in units of your planned risk per trade | Converts price movement into standardized risk terms (e.g., 1.8R left behind) |

These metrics help answer:

• Are you exiting winners too early, leaving significant profit untaken?
• Are your stops being hit just before favorable reversals?
• Are you cutting losses at appropriate levels, avoiding larger drawdowns?

Filters – Narrow the Analysis

Apply filters at the top of the tab to focus the view:

• Portfolio — All or single portfolio
• Setup — All or multi-select specific setups
• Side — Long, Short, or Both
• Date Range — From/To (defaults to last 6–12 months or matches current Council audit)
• PEE Window — Time period after exit to evaluate movement (e.g., session end, 0–30 min, 31–60 min, 61–120 min, 120+ min)
• Volatility Regime — All, High Volatility Only, Normal, Low Volatility Only (uses ATR(14) or proxy if not explicitly tagged)

The Three Sub-Tabs – Structured Exit Review Workflow

1. Overview & Sequence (Recommended Starting Point)
   • Exit Intelligence Summary cards:
   • Percentage of optimal exits
   • Average R left on winners (MFPE)
   • Average R saved on losers (MAPE)
   • Estimated potential extra profit ($)
   • Distribution of PEE windows and session cutoff impact
   • PEE Window Correlation Table — Shows how sequence and magnitude change with different post-exit time windows.
   • Volatility Regime Breakdown — MFPE/MAPE values and sequence distribution by high/normal/low volatility conditions.
   • Exit Habits Table — Performance grouped by auto-detected exit type (Stop Hit, Target Hit, Manual, Trailing, etc.).
   • Sequence Distribution Charts — Bar charts showing % of MFPE-first vs MAPE-first outcomes (separated for winners and losers).
   • Setup Breakdown Table — Metrics by setup, including sequence %, average MFPE/MAPE in R terms, trade count, and Optimize button (opens simulation tool).
   • Latest Trade Diagnoses — Recent trade insights + cumulative R left on table across the filtered set.

1. Exit DNA (Visual Pattern Detection)
   • Scatter chart where each point represents one trade.
   • Axes typically: MAPE (R) vs MFPE (R).
   • Color/size by outcome (winner/loser), setup, or classification (“Optimal,” “Early Exit,” “Tight Stop”).
   • Tooltip on hover/click: symbol, setup, exact values, realized R, classification.
   • Zoom controls: Full view, cluster focus, or detail zoom into specific patterns (e.g., high MFPE cluster).

Use this to visually identify clusters of similar exit behavior.

1. The Oracle (Rule Simulation & Recommendation Engine)
   • Test hypothetical exit rule adjustments on your filtered trades:
   • Move stop to breakeven at +X R
   • Widen stops by Y R
   • Take Z% profit at +W R, trail the remainder
   • Trail by N R after +1R
   • Output per simulated rule:
   • Number of trades affected
   • Average gain/loss change per affected trade (in R)
   • Projected annual R impact
   • Performance by regime (does it help/hurt in high volatility?)
   • Trade-offs and risks
   • Recommended setups to apply the rule to
   • Deploy button: Generates formatted rule text (execution logic, rationale, expected impact, regime notes) ready to copy into Setup settings.

Data Requirements & Empty States

• Requires completed trades with valid PEE data (MAE/MFE during trade + MAPE/MFPE post-exit + sequence or window).
• Volatility regime accuracy improves with explicit tags; otherwise uses ATR(14) proxy.
• Empty state messages appear if: no qualifying trades, filters too narrow, or insufficient PEE data.

Recommended Workflow

1. Open PEE Analytics → apply filters (e.g., last 6 months, primary portfolio, all setups).
2. Start in Overview & Sequence → review Sequence Distribution → high MFPE-first on winners? → targets or trails may need adjustment.
3. Check Setup Breakdown → identify weakest setup → click Optimize → test rule adjustments.
4. Move to Exit DNA → look for visual clusters of early exits or tight stops.
5. Review Volatility Regime Breakdown and PEE Window Correlation → understand time/regime effects.
6. In The Oracle → select a high-confidence rule → review projected impact → Deploy to the relevant setup.
7. Re-run after 20–30 new trades → observe changes in sequence percentages and key metrics.

Quick Reality Checks

• High percentage of MFPE-first on winners? → Exits may be too early — consider widening targets or adding trailing logic.
• High percentage of MAPE-first on losers? → Stops may be too tight — test widening in specific regimes.
• No meaningful rule improvements in Oracle? → Exit issues may stem from upstream problems (entry timing, regime selection).
• Tab empty? → Insufficient PEE data (ensure MAE/MFE/MAPE/MFPE are populated) or filters too restrictive.

Next Episode: Full Flywheel Mastery – Integrating Weekly Alpha Report → Calendar Analytics → CharterElite Analytics (PEE, Exit DNA, Oracle) → AI Council Audit → Reality Check → Action Plan → Setup DNA → Evolution into a closed-loop system that steadily elevates performance without increasing risk or chasing new strategies.

Open CharterElite Analytics → PEE Analytics now. Review the Sequence Distribution charts. High MFPE-first percentage on winners?

That represents capital left on the table after your exits. The Oracle already has rule simulations ready to test.

Select one high-confidence adjustment. Deploy it to the relevant setup. Track the change in the next audit.

Your exits are not fixed by fate. They are data-driven decisions. Improve them systematically.

Your performance is ready for this level of precision. Take the next step.