Lean Cloud Backtestv0.3.3

A quantitative analyst builds a Python research environment using QuantBook to fetch historical A-share data from East Money, compute a custom factor (e.g., MACD golden cross), and run a backtest with next-bar execution. The pipeline covers data collection, factor computation, target selection, and visualization, all within the lean-cloud-backtest skill.

A researcher uses the C# QuantBook environment to connect to multiple data providers (e.g., Joinquant), fetch historical data for Hong Kong stock entities, calculate Bollinger Bands, and store the results for later backtesting. The skill ensures proper entity ID formatting and semantic lock compliance.

A crypto trader leverages the skill to backtest a volume breakout strategy on crypto data (e.g., crypto_btc). The pipeline collects data from a free source, applies a transformer before accumulator in the factor pipeline, and uses the precomputed MACD parameters locked by the skill. The output includes signals and performance metrics.

A user with minimal coding experience describes a strategy in natural language to a Claude Code agent, which triggers the lean-cloud-backtest skill. The agent asks for market (A-share), data source (East Money), strategy type (MA crossover), and time range, then generates code and executes the backtest automatically.

A quantitative developer integrates the skill into a larger pipeline and runs the anti-pattern checks (e.g., AP-ZVT-183 for dividend factor issues) before deployment. The skill's evidence quality notice prompts a manual review of critical decisions, ensuring robustness in production backtesting.

A fintech company offers the Lean Cloud Backtest skill as part of a subscription service for individual quants and small hedge funds. Users pay a monthly fee for access to the automated backtesting pipeline, data connector library, and compliance with semantic locks.

A consulting firm uses the skill to quickly prototype and backtest custom strategies for clients (e.g., asset managers). They leverage the SOP version and reference files to ensure reproducibility and adherence to constraints, delivering production-ready code.

A brokerage integrates the skill as a white-label backtesting tool for their clients, enabling them to test strategies on the broker's data (e.g., QMT). The brokerage pays a licensing fee and customizes the skill's target selection and execution modules.