AI Agent Skills

Codera Quant is a Java framework for algorithmic trading strategies development, execution and backtesting via Interactive Brokers TWS API or other brokers API

Python library for backtesting technical/mechanical strategies in the stock and currency markets

Review portfolio-company financial packages, flag variance to plan, and prepare monitored KPI summaries.

Complete and link income statement, balance sheet, and cash flow statement model templates with formulas and checks.

Commission, slippage, and market-impact cost models for realistic strategy simulation. Use when backtesting to ensure P&L accounts for transaction costs.

Event-driven backtesting with realistic order execution, position tracking, and performance measurement. Use when simulating a trading strategy on historical data.

Open-source investment research platform for financial analysis agents.

Stock analysis skill for market research and financial reasoning workflows.

Build roll-adjusted continuous futures series without artificial price jumps. Use when backtesting futures strategies that span contract rollovers.

Estimate a public companys intrinsic value through DCF, relative, and sum-of-the-parts valuation methods.

Prepare a pre-earnings briefing with consensus estimates, beat-miss history, and current market context.

Create a global market conditions report spanning equities, rates, FX, commodities, risk appetite, and sector rotation.

Rank recent market-moving news and explain likely effects on equities, commodities, monetary policy, and earnings.

Research a US equity across fundamentals, valuation, technical context, peers, and current market data.

Look up A-share, Hong Kong, and US stock tickers and retrieve historical OHLCV price data for research.

Analyze a ticker with AlphaGBMs five-pillar framework: fundamentals, technicals, sentiment, flows, and valuation.