zenith-execution

name	zenith-execution
description	Unified trade execution suite: Half-Kelly sizing, stop-loss calculation, Monte Carlo simulation, and portfolio rebalancing. Absorbs: kelly-mandate, stop-loss-calc, monte-carlo-sim, portfolio-rebalancer.
argument-hint	setup | sizing | simulate | rebalance | optimize <ticker>
allowed-tools	["Read","Bash","WebFetch"]
auto-invoke	true
model	default
context_trigger	position sizing, Kelly, stop loss, Monte Carlo, simulate, rebalance, trade execution, portfolio optimization

ZenithFX Execution Suite (Expanded)

Absorbs: kelly-mandate, stop-loss-calc, monte-carlo-sim, portfolio-rebalancer

Unified quantitative execution skill for High Win-Rate trading systems (Protocol 367).

Triggers

"zenith", "trade setup", "position size", "stop loss", "kelly criterion", "how much to risk", "invalidation point", "simulate", "monte carlo", "rebalance", "portfolio allocation"

Sub-Commands

1. Position Sizing (Half-Kelly)

1. Demands Win Rate, Reward:Risk, and Total Capital.
2. Computes Full Kelly (theoretical optimum).
3. Halves it (Half-Kelly) for psychological variance and execution error.
4. Hard caps at 10% regardless of edge.

2. Stop-Loss (Structural Invalidation)

1. Identifies the price where the trade premise is demonstrably false.
2. Calculates distance between Entry and Invalidation.
3. Fits pre-determined Capital Risk % into that distance → Position Size.

Rule: A Stop Loss is a structural invalidation point, not an arbitrary budget allowance.

3. Monte Carlo Simulation

Simulates N independent trades through a given structure.

Inputs: Win Rate (%), Risk:Reward, Risk per trade (%), Number of trades (N), Starting capital.

import random

def monte_carlo(wr, rr, risk_pct, n_trades, starting_capital, n_paths=1000):
    results = []
    ruin_count = 0
    max_drawdowns = []
    for _ in range(n_paths):
        equity = starting_capital
        peak = equity
        max_dd = 0
        for _ in range(n_trades):
            if random.random() < wr:
                equity += equity * risk_pct * rr
            else:
                equity -= equity * risk_pct
            peak = max(peak, equity)
            dd = (peak - equity) / peak
            max_dd = max(max_dd, dd)
            if equity <= starting_capital * 0.2:
                ruin_count += 1
                break
        results.append(equity)
        max_drawdowns.append(max_dd)
    results.sort()
    return {
        "median": results[len(results)//2],
        "p5": results[int(len(results)*0.05)],
        "p95": results[int(len(results)*0.95)],
        "max_dd_median": sorted(max_drawdowns)[len(max_drawdowns)//2],
        "ruin_probability": ruin_count / n_paths,
        "double_probability": sum(1 for r in results if r >= starting_capital * 2) / n_paths,
    }

Output:

Monte Carlo Simulation (1,000 paths × N trades)
─────────────────────────────────────────────
Structure: WR=60%, RR=1.0, Risk=1.1%/trade
Starting Capital: $10,000

Median Terminal Equity:  $XX,XXX
5th Percentile:          $X,XXX
95th Percentile:         $XX,XXX
Max Drawdown (median):   XX.X%
P(Ruin):                 X.X%
P(Double):               XX.X%
─────────────────────────────────────────────
Verdict: [PASS/FAIL] — structure is [robust/fragile]

4. Portfolio Rebalance

1. Evaluates current allocation weights vs initial target weights.
2. Identifies momentum drift.
3. Outputs specific buy/sell orders to restore Kelly/Structural parity.

Reference Protocols

• Protocol 367: High Win-Rate Supremacy
• Protocol 368: Five Levers
• Protocol 46: Trading Methodology