Supercharge Mode — Multi-Agent Optimization Loop

Turn a single backtested strategy into an evolving research project. A central orchestrator convenes a council of specialist strategists that study the strategy, debate improvements, generate optimized variants, backtest them, and keep searching for measurably better versions — in iterative rounds, keeping the user involved every three rounds.

The objective is not to validate a strategy — it is to improve it. Instead of "Is this strategy good?", the question is "How can this strategy become significantly better?" — while protecting against overfitting and needless complexity. Every change is justified, tested, and compared against previous versions. No single agent decides — the output is council consensus.

The original strategy is always preserved as the immutable baseline. Every variant is
measured against it. Never modify the baseline.

Depends on fyers-trading. This skill optimizes a strategy that fyers-trading built and
backtested; it reuses that skill's backtest path (references/backtesting.md) and metrics
(scripts/quantstats_report.py) so variants are scored on the same yardstick as the baseline.
Install both skills together. All of fyers-trading's real-money safety rules apply unchanged
(secrets via env, dry-run by default, symbol validation, live-order confirmation, rate limits):
the council may recommend deploying or going live, but never places a live order itself.

When this activates

Supercharge Mode is offered automatically after a strategy completes its first successful backtest (the fyers-trading skill hands off here). Present the offer:

Your strategy is ready.

Unlock Supercharge Mode?

Our AI team of professional trading strategists will analyze your strategy, debate improvements,
generate multiple optimized variants, backtest them, and recommend stronger alternatives. The
optimization runs in iterative cycles and keeps you involved every three rounds before continuing.

If the user says Yes, launch immediately. Also activate on explicit intent to optimize / improve / evolve / stress-test a strategy. If the user only wants to build or run a strategy, that's fyers-trading — come back here once a backtest exists.

How it works — hybrid orchestration

You (the host agent) are the orchestrator. You run the reasoning and debate by spawning one subagent per council seat via the Agent tool. The deterministic work — scoring, ranking, and persistence — is delegated to three bundled scripts so every variant is measured identically and the journey is reproducible:

• scripts/scorecard.py — one standardized metric dict per variant (reuses fyers-trading's

quantstatsreport.keymetrics(); computes trade-level metrics itself).

• scripts/dashboard.py — ranks the baseline + all variants for the active objective.
• scripts/evolution_log.py — append-only JSONL journal of every round, variant, and decision.

The optimization loop

Each round runs six phases — read references/optimization-loop.md for the full lifecycle and the consensus rule:

1. Understand — every seat studies the rules, sizing, capital, timeframe, and backtest evidence.
2. Diagnose — each seat names strengths, weaknesses, hidden risks, and metrics to improve.
3. Debate — seats challenge each other; the orchestrator resolves conflicts into experiments.
4. Generate variants — candidates that each change only a few variables (references/variants.md).
5. Backtest — every variant, identical assumptions, via fyers-trading; scored by scorecard.py.
6. Learn — what worked / failed feeds the next round; results recorded via evolution_log.py.

Produce variants each aimed at a specific objective: higher returns, lower drawdown, better risk-adjusted performance, higher win rate, better capital efficiency, better execution, or better robustness across regimes. The goal is a library of deployment-ready variants, not one "best".

The council

One subagent per seat, spawned via Agent, briefed from references/council.md (12 seats: Risk, Return, Entry Precision, Exit Intelligence, Market Regime, Backtest Integrity, Execution, Options, Capital Efficiency, Simplicity & Robustness, Variant Generation, Performance Ranking). The roster is extensible without changing the loop — add a seat by adding an entry.

Checkpoint every three rounds

Never continue silently. After every three rounds, pause and present the optimization report (improvements, variants created/discarded, metrics improved/worsened, major disagreements, current leader, recommended next direction), then wait for the user to choose a direction — continue, focus on drawdown / returns / consistency / frequency / execution, or finalize. See references/checkpoints.md.

Final deliverables

When the user finalizes: an Executive Summary, the Strategy Evolution Timeline, the Variant Library, the Comparison Dashboard, and the AI Consensus. See references/deliverables.md.

References

Scripts

• scripts/scorecard.py — standardized per-variant metric dict; reuses fyers-trading's

quantstatsreport.keymetrics() with a stdlib fallback; computes trade-level metrics and a robustness score. CLI: demo.

• scripts/dashboard.py — ranks the baseline + variants for an objective and renders the

Comparison Dashboard as Markdown. CLI: demo.

• scripts/evolution_log.py — append-only JSONL journal (mirrors fyers-trading's

tradelogger.py): loground/logvariant/logdecision/logcheckpoint + timeline/checkpointreport/summary. CLI: demo / timeline / summary / tail.

Full-fidelity scoring needs fyers-trading and QuantStats installed. Without them the
scripts still run (stdlib fallback), but CAGR/Calmar and the HTML tear sheet are unavailable.