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Swarm V4 - God Mode

EVOLUTION STRUCTURE

How a champion is born. Total autonomy, fifty generations, crime scene memory, weekend tournaments, and a single Hall of Fame. No human in the loop.

Fifty generations. Thirty explore. Twenty hone.

Every cycle the swarm births fifty candidates. The first thirty are wide-exploration mutations - the LLM is given total authority to rewrite every scaffold function and invent brand-new multi-dimensional engines. Generations thirty-one to fifty pivot to honing: the strongest candidate so far is handed back to the LLM as a parent for surgical improvement. Whatever emerges top of the leaderboard becomes the next champion.

01
Exploration Phase · Gen 1-30
Wide mutation. The LLM receives the crime scene memory plus the shadow matrix and invents novel structural engines. No parent, no template. Total authority.
02
Honing Phase · Gen 31-50
The leader so far becomes the parent. The LLM is instructed to mutate surgically, keeping what works and cutting what doesn’t. Twenty focused evolutions to sharpen the apex.
03
Evaluation · 6-Day Backtest
Every candidate is tested against the most recent six days of live market data with realistic fees (0.06 percent taker plus 0.02 percent slippage), eight concurrent positions, 1.4 percent risk per trade, and 3× leverage. The scorer ranks by final PnL in dollars.
04
Coronation
If the best candidate beats the live champion’s re-scored performance, it is copied to the Hall of Fame and becomes the new live strategy. Otherwise the reigning champion survives another cycle.

The LLM rewrites every function.

The V4 swarm hands the LLM a bare scaffold and tells it to rewrite all of it. pre_filter, signal_entry, manage_position, the cache precompute, the DNA dictionary, even the hypothesis docstring that ends up on the Hall of Fame page. No template lock-in. No fixed indicator list. If the LLM wants Shannon Entropy and Hurst persistence instead of ADX, it writes them. The only hard constraints are pure NumPy execution, honest physics (one-hour window lookups, four-hour time exits), and a fifty-percent minimum win rate for survival.

Paradigm
God Mode Master Synthesis. The LLM invents the entire engine per generation.
Model
Gemini 3.1 as the forensic quant engineer. Opus 4.7 audits the architecture plan and commits the code.
Mandates
Pure NumPy. One-hour cache windows. Four-hour time exits. Fifty-percent win-rate floor. Rich DNA. Hypothesis docstring.

Every loss teaches the next generation.

Live losing trades are written to a structured crime scene log. Before each swarm cycle fires, the LLM receives the top recent failure patterns as part of the prompt — the exact chop bands, ADX zones, volume ratios, and regime labels that liquidated the previous champion. The result is that each new generation is mathematically aware of what killed its predecessors and builds vetoes accordingly. The Gamma Goliath’s “Chop Death Zone” filter was born this way, hard-coding a refusal to short in the 40-60 chop / 20-35 ADX band where four prior champions were liquidated.

The swarm also learns from what it missed.

Every signal that fires without the filter stack approving is logged to a shadow matrix. If a shadow signal would have made more than 2 percent profit, it counts as missed alpha. The ten most recent misses are fed back into the swarm prompt alongside the crime scenes. This closes the loop — the bot learns not just what to avoid, but also what it is wrongly rejecting.

Saturday evening. Every week.

The swarm fires on a systemd timer at 17:00 BST every Saturday. Fifty generations run unattended over roughly three to four hours. The crowned candidate is validated against the live adapter, added to the Hall of Fame, and deployed. The previous champion is archived, not deleted. This means every week the system is rewritten from scratch against the latest six-day market window. No champion stays on the throne unless it continues to beat its own successors.