Trader Hand Skill

• You use or plan to use Alpaca for paper or live trading and want your assistant to place and manage orders on your behalf.
• You want real-time market analysis combining multiple indicators (RSI, MACD, Bollinger Bands, VWAP) with free data sources like Yahoo Finance and CoinGecko.
• You need disciplined position sizing and risk management enforced automatically — never risking more than your defined percentage per trade.
• You want to track trades in a structured journal and improve prediction accuracy over time using Brier score calibration.

• You use a broker other than Alpaca — the execution layer is built exclusively around the Alpaca REST API.
• You need real-time streaming data or sub-second execution; this skill uses REST polling, not WebSocket streams.
• You are looking for fully automated algorithmic trading without human review — this skill is designed to assist and inform, not replace human decision-making.

Includes step-by-step calculation formulas and worked examples for RSI, MACD, Bollinger Bands, VWAP, Moving Averages, ATR, OBV, and volume analysis. Interpretation tables cover signal strength, divergence patterns, and best-practice usage rules.

Ready-to-run curl templates cover every major Alpaca endpoint: account info, positions, market/limit/stop/bracket/OCO/trailing-stop orders, order management, position closing, historical and live market data, and the market clock. Crypto trading (BTC/USD, ETH/USD, SOL/USD) is fully supported.

Built-in formulas for fixed-fractional position sizing, Kelly Criterion (with half- and quarter-Kelly guidance), parametric VaR, Sharpe and Sortino ratios, maximum drawdown, Calmar ratio, profit factor, and per-trade expectancy — all with worked numerical examples.

A 10-point pre-trade checklist enforces trend alignment, signal quality, stop placement, position sizing, correlation checks, and catalyst awareness before every entry. A structured JSON trade journal template supports post-trade review and confidence calibration.

Based on Philip Tetlock's superforecasting methodology, the skill includes a confidence level guide, Brier score calculator, and a self-check protocol for recalibrating prediction accuracy after every 50 resolved trades.

A curated reference of free data sources for price data, fundamentals, sentiment (CNN Fear & Greed, VIX, Put/Call ratio), macro data (FRED, BLS, CME FedWatch), and crypto-specific metrics (CoinGecko, DefiLlama, Bitcoin Fear & Greed index).

Fetch overnight price action via Alpaca's data API, calculate RSI and MACD inline with Python, check the Fear & Greed index, and produce a ranked watchlist with entry, stop, and target levels before the market opens.

Given an entry price and ATR value, the assistant calculates the correct position size based on your portfolio value and risk-per-trade setting, then places a bracket order with stop-loss and take-profit legs in a single API call.

Pull all open Alpaca positions, compute unrealized P&L, check sector concentration against the 30% maximum guideline, flag correlated holdings (e.g., holding AAPL + MSFT + GOOGL simultaneously), and summarize total portfolio heat.

Log completed trades with confidence scores and setup tags using memory_store, then compute Brier scores across your prediction history to identify whether you are systematically overconfident or underconfident and adjust accordingly.