35 skills · sorted by relevance
Design backtests that evaluate trading strategies against historical data. Account for trading costs, avoid using future information or excluding delisted securities, and compare strategies using separate development and evaluation periods.
Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
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Apply backtesting frameworks in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses backtesting frameworks or its strengths fit the task.
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Build algorithmic trading systems — backtesting, strategy development, live execution, and risk management. Use when tasks involve backtesting trading strategies, connecting to exchange APIs (Binance, Alpaca, Interactive Brokers), implementing technical indicators, portfolio optimization, order execution, risk management rules, market data processing, or building trading bots. Covers both crypto and traditional markets.
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Retrieve minute-level price data for stocks and cryptocurrencies, calculate average prices and volume patterns, and test intraday trading strategies. Configure the candle interval and trading costs to examine results using historical market data.
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
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Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
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Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strateg...
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Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
The English summary is being prepared. Showing the original description.
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
The English summary is being prepared. Showing the original description.
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
The English summary is being prepared. Showing the original description.
Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
The English summary is being prepared. Showing the original description.
Develop AxonX plugins and manage quantitative finance research through the command line or MCP, an agent connection interface. Track data preparation, training, prediction, and historical strategy tests using execution status, logs, artifacts, and dependencies.
Convert a Vibe-Trading backtest strategy or a written trading strategy into a Python class for vnpy. Map indicators and trading signals to vnpy conventions, with asset-specific order handling and instructions for loading the strategy for backtesting or live trading.
Backtest stock and cryptocurrency option strategies using theoretical prices derived from underlying prices. Simulate combinations of option positions, track profit and loss, and export Greeks, measures of sensitivity to price, volatility, and time.
Analyze option strategies with profit and loss charts, breakeven points, and maximum risk calculations. Compare single options and combinations, and explore how underlying prices and volatility affect theoretical values for research and backtesting.
Turn trading rules into Python code and configuration files, then test them against historical data. Review performance, position sizing, and trading costs, and revise strategies using the results produced by the backtest engine.
Turn academic papers and research reports into investment signals and trading strategies. Coordinate existing tools to extract formulas, implement rules, test them on historical data, store results, and monitor whether their effectiveness declines over time.
Analyze crypto derivatives for research and backtesting using perpetual funding payments, futures prices across expiry dates, and options volatility. Organize market metrics, compare strategy candidates, and outline risks such as margin shortages, funding reversals, and exchange failures.
Evaluate investment factors across multiple securities using statistical measures and grouped backtests. Compare how well each factor predicts future returns, examine its consistency over time, and combine useful factors with equal or performance-based weights.
Research stock and cryptocurrency strategies through historical testing, factor analysis, and options pricing. Analyze trading journals, extract rules from profitable trades, and generate reports comparing simulated results with actual trading outcomes.
Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.
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Separate a strategy return series into declared baseline exposure and residual edge with returns-based OLS attribution, HAC inference, rolling stability, alternate-baseline sensitivity, and regime breakdowns. Use when evaluating whether backtest, out-of-sample, or live returns contain independent alpha beyond market, equal-weight, momentum, sector, or user-supplied factor returns; when explaining whether a drawdown came from baseline exposure or strategy-specific behavior; or when a strategy needs an attribution quality gate after backtesting. Do not use for holdings-based Brinson attribution, feature-level Shapley explanations, or analysis from summary metrics without a dated return series.
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High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics
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