SIMBA#

SIMBA#

SIMBA# is an open-source software toolkit for quantitative finance and algorithmic trading. It provides tools for data analysis, backtesting, automated trading, and more to help traders and quants research and execute trading strategies.
SIMBA# screenshot

SIMBA#: Open-Source Toolkit for Quantitative Finance & Algorithmic Trading

A comprehensive software toolkit for data analysis, backtesting, automated trading, and more, helping traders and quants research and execute trading strategies.

What is SIMBA#?

SIMBA# is an open-source software toolkit designed specifically for quantitative finance and algorithmic trading. It provides a wide range of tools and functionality that allow traders, quants, and financial analysts to backtest trading strategies, analyze market data, create automated trading systems, and more.

Some of the key features of SIMBA# include:

  • Backtester for testing and optimizing trading strategies across various asset classes including equities, futures, forex, and options.
  • Charting and data analysis tools for visualization and statistical analysis.
  • Algorithmic trading engine for automating trading strategies and integrating with brokers and exchanges.
  • Python bindings for scripting custom indicators, strategies, and analytics.
  • Portfolio optimization, risk management, and performance attribution capabilities.

By providing this comprehensive toolset focused specifically on algorithmic trading in an open-source package, SIMBA# aims to make algorithmic trading more accessible. The software supports live trading and paper trading simulation modes so strategies can be researched, tested, and executed effectively. Overall, SIMBA# provides quants and traders with an institutional-quality platform for quantitative analysis and automated trading system development.

SIMBA# Features

Features

  1. Data analysis tools
  2. Backtesting engine
  3. Automated trading
  4. Strategy research tools

Pricing

  • Open Source

Pros

Open source and free

Wide range of tools for quant finance

Active community support

Cons

Steep learning curve

Limited documentation

Not as full-featured as commercial platforms

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