Monte Carlo Crypto Core

This skill enables you to run sophisticated Monte Carlo simulations for trading strategies. It helps answer questions like: "If I buy BTC at $70,000 with 5% daily volatility and put a stop loss at $65,000, what is the probability I get stopped out within 30 days?"

Setup

1. Install requests: pip install requests
2. Set the SKILLPAY_API_KEY environment variable with the skill owner's SkillPay API key.

Billing (0.001 USDT per call via SkillPay)

This is a paid skill. Every simulation call requires the --user USER_ID parameter. If a call fails with a payment_url, you must show this link to the user.

Tool: Run Monte Carlo Simulation

Command:

python scripts/montecarlo.py --user USER_ID --price 70000 --vol 0.04 --drift 0.001 --days 30 --paths 5000 --stop-loss 65000 --position long

Required Parameters: - --user: The user's ID for billing. - --price: The current price of the asset. - --vol: The estimated daily volatility as a decimal (e.g., 0.04 for 4%). You can derive this from historical data standard deviation.

Optional Parameters: - --drift: The expected daily return. Default is 0.0. Positive drift favours longs, negative favours shorts. - --days: Number of days to simulate into the future. Default: 30. - --paths: Number of simulation paths to run. Higher is more accurate but slower. Max is 20000, Default 10000. - --position: long or short. Default is long. - --stop-loss: The exact price level where the position gets liquidated or closed for a loss. - --take-profit: The exact price level where the position closes in profit.

Output: Returns a JSON object containing expected prices, 5th/95th percentile worst/best case scenarios, and risk_metrics including the exact probability of hitting the stop loss (hit_stop_loss_pct) and overall win probability.

Use these probabilities to justify your trading advice to the user. Do not recommend trades with a win_probability_pct lower than 50% unless the user explicitly accepts high risk.