The goal is not coverage of every idea, but:

a small, orthogonal set of strategies that map cleanly to distinct market regimes

🧠 First principle

A strategy should exist only if it answers:

“This behaves fundamentally differently from the others under different conditions.”

If two strategies differ only by parameters → they should be one.

🧱 The minimal useful axes

You can reduce the entire market behavior space to a few dimensions:

Trend vs Range
Volatility (low / expanding / high)
Event pressure (normal vs event-driven)
Liquidity quality (normal vs thin/chaotic)

That’s enough to define your taxonomy.

🎯 Minimal Strategy Set (clean + complete)
1. 🟢 idle / risk_off

Purpose: Do nothing / capital preservation

When:

high uncertainty
conflicting signals
extreme event risk
broken execution conditions

This is NOT optional.
This is what prevents dumb trades.

2. 🔵 trend_following

Purpose: Ride directional moves

When:

clear structure (higher highs / lower lows)
sustained momentum
decent liquidity

Variants are internal, not separate strategies:

slow trend
strong trend
3. 🟡 mean_reversion

Purpose: Trade inside ranges

When:

sideways market
low volatility
no directional conviction

Key trait:

fades extremes, expects reversion
4. 🟣 breakout

Purpose: Capture volatility expansion

When:

compression → expansion
levels about to break
volatility increasing

Important:
This is NOT trend-following.
It’s anticipating the move, not riding it.

5. 🟠 grid / liquidity_harvesting

Purpose: Monetize noise

When:

choppy, bounded markets
decent liquidity
no strong directional bias

Difference to mean reversion:

systematic order placement
not signal-based entries
6. 🔴 event_driven

Purpose: React to external shocks

When:

major news
macro releases
sudden sentiment shifts

Behavior:

fast, defensive or opportunistic
often temporary
7. ⚫ defensive / unwind

Purpose: Reduce exposure safely

When:

regime shift detected
strategy mismatch
execution degradation

This is a transition strategy, not alpha-seeking.

🧬 Why this set works

You now cover:

Regime	Strategy
Trend	trend_following
Range	mean_reversion
Chop	grid
Expansion	breakout
External shock	event_driven
Uncertainty	idle
Transition/risk	defensive

👉 That’s the full space, without redundancy.

⚙️ Strategy metadata (critical for Hermes)

Each strategy should declare its “natural habitat”:

{
  "name": "mean_reversion",
  "expects": {
    "trend": "none",
    "volatility": "low",
    "event_risk": "low"
  },
  "avoids": {
    "volatility": "expanding",
    "event_risk": "high"
  },
  "risk_profile": "medium"
}

Hermes uses this—not gut feeling.

🧠 Hermes selection logic (conceptual)

Hermes computes current state:

{
  "trend": "none",
  "volatility": "low",
  "event_risk": "low"
}

Then scores strategies:

{
  "mean_reversion": 0.9,
  "grid": 0.7,
  "trend_following": 0.1
}
🔁 Preventing flip-flopping (VERY important)

Without this, Hermes will go insane.

Add:

1. Hysteresis
don’t switch unless new strategy is significantly better
2. Minimum hold time
once active, stay active for X minutes unless strong signal
3. Confidence threshold
only switch if confidence > e.g. 0.65
🧭 Subtle but powerful distinction
mean_reversion vs grid

This is where many systems fail:

mean_reversion = signal-based
grid = structure-based

Keep both.

🧪 Optional later (DO NOT start with this)

Only after everything works:

volatility_scalping
cross-asset arbitrage
market-making

But don’t pollute v1.

💡 Naming matters more than you think

Use names that are:

intuitive
explainable to humans
stable over time

Avoid:

“alpha_v3_dynamic”
“strategy_7”
🔚 Final minimal set (clean version)
1. idle
2. trend_following
3. mean_reversion
4. breakout
5. grid
6. event_driven
7. defensive
🧠 What you just achieved

With this taxonomy:

Hermes has a finite decision space
Strategies remain coherent and specialized
Explanations become natural language
Debugging becomes possible