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Entry Conditions vs. Statistical-Edge Conditions

The two most misunderstood components of systematic trading — and why they must be kept separate.

Most traders treat the rules that create an entry signal as if they’re the same rules that define the strategy’s edge.

They are not.

In a fully researched, statistically grounded trading system, “entry conditions” and “edge conditions” perform completely different roles and evolve differently over time.

This document explains the difference and why your risk management — especially stop-loss and take-profit design — depends on keeping these concepts separate.

1. What Are Entry Conditions?

The rules that decide when a position is opened.​

Entry conditions are a fixed, well-defined set of logical criteria that must evaluate to “true” at the moment of entry.

Examples:

  • SMA slope ≥ 0.10
  • breakout above a volatility envelope
  • compression + expansion pattern
  • funding within a threshold
  • trend filter = aligned
  • secondary momentum condition

Entry conditions are:

  • binary
  • momentary
  • evaluated once
  • designed to trigger trades
  • NOT used to model hold horizon
  • NOT used to determine stop distance
  • NOT used to manage the trade

Entry conditions answer only one question:

Should I enter right now?

Once the position is open, the entry logic stops being relevant for managing the position.

2. What Are Statistical-Edge Conditions?

The rules that determine how long your edge lasts after entry.​

Where entry conditions are binary and instantaneous, statistical-edge conditions are probabilistic and time-evolving.

They describe how the trade is expected to behave after entry based on historical distributions.

They include:

  • drawdown distribution after entry
  • return distribution after entry
  • slope persistence / momentum decay
  • continuation probability
  • volatility expansion / compression behavior
  • bar-by-bar regime transition probabilities
  • how fast the entry signal decays
  • the expected minimum hold horizon

These metrics change at each time slice (bar 1, bar 2, bar 3…).

Example:

At entry:

SMA slope = 0.10

One bar later:

  • expected slope = 0.07
  • drawdown distribution shifts
  • continuation probability drops
  • volatility regime adjusts

The entire statistical surface changes, even though your original entry conditions may still be true.

3. Time-Slice Updating Is Crucial

Probability distributions must be recalculated for each bar after entry:

  • the expected adverse movement changes
  • the expected return changes
  • the likelihood of continuation changes
  • the signal’s strength decays
  • the transition probabilities shift

This is why your expected hold horizon isn’t arbitrary:

It is the interval during which the statistical-edge conditions retain meaningful positive expectation.

Your stop and take-profit levels must respect this window — not the entry rule set.

4. Why This Distinction Matters

A. Stops must be built using edge behavior, not entry conditions.​

Stops must survive:

  • maximum expected drawdown
  • volatility during the edge window
  • indicator decay behavior

They DO NOT depend on:

  • the entry trigger
  • ATR heuristics
  • swing highs/lows
  • static structure

📄 See: Strategy-Specific Stop Modeling

B. Take-profits must align with edge decay, not entry logic.​

The moment the edge decays, the trade no longer has positive expectation.

TP targets must reflect:

  • time until edge decay
  • continuation probabilities
  • convex payoff design

📄 See: Strategy-Specific Take Profit Modeling

C. Position sizing happens AFTER the edge mechanics are understood.​

Once you know:

  • stop distance
  • edge lifetime
  • expected gain window

…then you size the position.

📄 See: Position Sizing & MLPT

D. Misunderstanding this distinction destroys most retail strategies.​

Retail traders unknowingly blend:

  • entry rules
  • stop logic
  • exit logic
  • sizing logic

…into one vague cloud of heuristics.

Professionals split them with surgical precision.

5. Summary: The Two Sets Are Not the Same

Entry Conditions​

  • binary
  • moment-specific
  • determine when you open a trade
  • fixed logic
  • NOT used for stops or TPs

Statistical-Edge Conditions​

  • probabilistic
  • time-evolving
  • determine how long your edge lasts
  • derived from backtested distributions
  • DO determine stops & take-profits

If your goal is to build strategies with predictable, scalable, repeatable performance, you must separate these sets conceptually and mathematically.

6. The Rule of Separation

Entry logic opens the trade. Edge logic manages the trade.

Confusing the two leads to incoherent stops, incoherent TPs, and incoherent sizing.

Keeping them separate produces:

  • stable risk profiles
  • optimal stops
  • optimal take-profits
  • consistent expectancy
  • convexity
  • predictable performance across regimes

This is the foundation of the entire Strategy Design process.

7. Edge Statistics Must Be Computed Before the Trade Opens

The moment your entry signal fires, you do not immediately send the open order.

Instead, the engine should perform:

edge_state = compute_edge_statistics(entry_time)

This computes:

  • expected minimum hold horizon
  • persistence probabilities
  • drawdown distribution for bars 1…N
  • continuation distribution for bars 1…N
  • expected reward distribution
  • volatility regime state
  • trade viability

Only after computing these do you know:

  • whether the edge is strong enough
  • whether the stop required is affordable given MLPT
  • whether the take-profit target is realistic within the hold horizon
  • whether the expected reward profile is convex enough

Only if the trade satisfies all these conditions should you actually open the position.

8. Once the Position Opens, Your Boundaries Become Fixed

After the trade opens:

You cannot expand the stop.​

Because:

  • MLPT is absolute
  • your risk allocation is fixed
  • widening the stop violates your risk model
  • widening the stop violates your strategy’s statistical structure

A widened stop turns:

  • a quantified strategy into
  • an unbounded discretionary exposure

You lose your edge immediately.

This is non-negotiable.

9. You May Reduce the Stop Distance If the Edge Collapses — With Caution

Once the position is open, the statistical-edge distributions update bar-by-bar.

If at some point:

  • adverse movement distribution worsens
  • continuation probability collapses
  • expected reward evaporates
  • slope or momentum collapses
  • volatility regime flips against you

…then it is statistically correct to reduce the stop.

However:

This is an advanced technique and can be dangerous if not done systematically.​

A premature tightening of stops risks:

  • exiting during normal volatility
  • misestimating continuation probability
  • violating the minimum-hold logic
  • reducing win rate dramatically

A conservative approach:

  • do not adjust the stop
  • unless the edge has decisively collapsed (probability < threshold)
  • and the statistical justification for holding has disappeared

This should be considered part of a strategy’s advanced version, not its baseline.

10. Take-Profit Adjustments Are Even More Sensitive

Take-profit behavior is tied directly to:

  • expected reward distribution
  • time-to-edge decay
  • convex payoff optimization

For beginners or early versions of a strategy:

TP1 should never be moved.​

If:

  • TP1 is hit → partial take
  • remainder follows secondary rules (trailing or fixed TP2/TP3)

You may choose to allow the later targets (TP2/TP3) more room, only if:

  • the slope decays gradually but remains positive (for longs)
  • the continuation distribution still favors forward movement
  • volatility regime remains supportive
  • expected return after TP1 remains statistically positive

But again:

This is advanced and not required for the first iteration of a strategy.​

11. Philosophical Note: KISS Is Better for Strategy V1

For early versions, the correct approach is:

  • fixed strategy-defined stop
  • fixed strategy-defined TP(s)
  • fixed position sizing rules
  • no dynamic trailing
  • no dynamic stop tightening
  • no mid-trade indicator re-optimization

Why?

Because:

  • it keeps win rate stable
  • reduces complexity
  • ensures strategy behavior remains predictable
  • simplifies edge computation
  • avoids false positives in “micromanaging” the trade

Once you have 300–1,000 trades logged, you can model:

  • dynamic stop tightening
  • dynamic TP behavior
  • volatility-adjusted exits
  • regime-switch adjustments

But not before.

12. Final Principle

The edge is defined before the trade opens. The SL/TP are defined before the trade opens. Small adjustments may occur only when the statistical edge collapses — and even then, with great caution.

This gives you:

  • discipline
  • consistency
  • predictable expectancy
  • risk integrity
  • mechanical repeatability
  • scalable execution

And it avoids the single biggest failure point in retail trading: moving stops and targets emotionally.