Feature Tour

// Generating a signal

It should be easy to explore and analyze any data you think might result in an opportunity, and to quickly turn your insights into the prototype of a signal, factor, or model. RapidQuant gives you tools that "get out of your way" so you can focus on your ideas instead of their implementation.

The high-productivity Python environment of RapidQuant is uniquely suited to signal generation, with rich interactivity, intelligent data alignment and handling of missing data, flexible reshaping, powerful data structures, and libraries to accomplish sophisticated financial analysis.

// Backtesting the results

RapidQuant fits the iterative workflow of financial research, letting you rapidly backtest your ideas and understand the results. Computations have been comprehensively optimized for performance so you won't have to wait, yet there's no compiling or mode-switching.

The backtesting framework of RapidQuant makes it easy to construct a portfolio, backtest, and understand performance & risk, while the powerful analytics library can be used to gain unique insight into the results.





// Loading and storing data

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RapidQuant's powerful and performant in-memory objects extend the open source pandas library to provide data structures for regular and irregular financial time series, data frames, and panel data, plus the tools to easily interface between memory and multiple storage formats, including Microsoft® Excel®, SQL databases, HDF5, web APIs, CSV and text files, even the clipboard.

// Data munging

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The pandas library also includes fast and efficient tools to manipulate data, including intelligent data alignment and handling of missing data, flexible reshaping and pivoting, label-based slicing, SQL-like operations, hierarchical indexing, date range generation and frequency conversion, date shifting and lagging, customizable time offsets, and much more.

// Linear algebra and statistics

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The open source NumPy and StatsModels libraries provides a huge range of features, including linear algebra, high-performance array processing on multidimensional arrays, fast Fourier transform, random number generation, plus many statistic and econometric models and tests, including regressions, linear models, descriptive statistics, moving window statistics and linear regressions, non-parametric tests, and much more.



// Factor transformation

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The factor transformation library allows you to standardize, winsorize, volatility adjust, volatility shrinkage, find industry/country mean and volatility adjustments, and more.

// Risk analysis

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RapidQuant includes a breadth of tools to understand risk, including ex-ante portfolio volatility and covariance, ex-ante CAPM beta, total and marginal contribution to risk, systematic and idiosyncratic contribution to risk, portfolio risk decomposition by factor category, and more.

// Portfolio construction

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The portfolio framework allows you to define risk and portfolio criteria in order to construct a portfolio for backtesting out of your tradable universe.



// Backtesting

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The framework for backtesting allows you to run a backtest on your portfolio against historical prices with your transaction cost assumptions.

// Return analysis

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The performance analytics library gives you the tools to understand summary and moving window performance, including summary statistics, covariance, correlation, skewness, kurtosis, semivariance, drawdowns, realized CAPM beta and alpha,historical VaR, historical expected shortfall, hit rate, average positive & negative return periods, Sharpe ratio, information ratio, Sortino ratio, Treynor ratio, updown ratio, upside potential ratio, Calmar ratio, and more.

// Performance attribution

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The performance attribution library allows you to deeply understand your model and portfolio, with BHB, Brinson-Fachler, ex-ante factor exposures, ex-post multi-variate factor loadings, regression based factor attribution, and return decomposition along size, volume, risk, and turnover quantile ranges.





// What Else Can I Do?

  • Technical analysis

    Gain access to a variety of moving averages, crossovers, and indicators (including, but not limited to, Bollinger Bands, stochastics, RSI, MACD, ADX, CCI, CMF, CMO, DPO, ROC, and CLV).
  • Develop production systems

    Because RapidQuant is comprehensively optimized for performance, with critical code paths compiled to C, it meets speed and stability requirements for most systems.
  • Integrate with C, C++, Fortran, and R

    Python's broad adoption in the scientific computing world means easy open source integration via Cython, f2py, and rpy2.
  • Optimization, GPUs, machine learning, and more

    Take advantage of the popularity of Python with the open source SciPy library and SciKits toolboxes to access a wide variety of additional functionality.

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