Kolleno
AI-driven accounts receivable automation to accelerate cash flow and optimize collection workflows.

Backtest and optimize algorithmic trading strategies with as few as three lines of code.
In the 2026 quantitative analysis landscape, fastquant remains a critical abstraction layer for financial data scientists. Built on top of Backtrader, it simplifies the complex process of backtesting trading strategies by providing a high-level Pythonic interface that reduces hundreds of lines of boilerplate code into simple, readable commands. Its technical architecture focuses on rapid prototyping; it integrates seamlessly with the Pandas ecosystem and supports automated data retrieval from sources like Yahoo Finance. For the 2026 market, fastquant positions itself as the primary 'fast-fail' validation tool for alpha generation, allowing analysts to iterate on technical indicators like RSI, MACD, and SMAC before transitioning to production-grade execution engines. The library's core philosophy is to democratize quantitative finance by lowering the barrier to entry for strategy validation, offering robust visualization tools and hyperparameter optimization out-of-the-box. As institutional trading shifts further toward data-driven signals, fastquant's role as a lightweight, open-source validator within CI/CD pipelines for trading bots has solidified its utility among both retail traders and boutique hedge fund analysts.
Encapsulates the entire Backtrader engine setup, data feed, and strategy execution into a single function call.
AI-driven accounts receivable automation to accelerate cash flow and optimize collection workflows.
The professional gateway to global multi-asset trading with institutional-grade API execution.
Total visibility and control over business spend with automated expense management and smart corporate cards.
Own shares of rental properties and vacation homes for passive income and long-term appreciation.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Native support for testing combinations of parameters to find the most profitable strategy settings.
Includes pre-coded logic for RSI, MACD, Bollinger Bands, and Moving Average Crossovers.
Direct hooks for Yahoo Finance and local CSV data sources with auto-formatting.
Generates comprehensive performance charts including equity curves and trade markers.
Allows developers to extend the BaseStrategy class to create proprietary logic.
Module support for correlating stock price movements with Twitter or news sentiment data.
Validating if an RSI strategy would have actually been profitable over the last 5 years.
Registry Updated:2/7/2026
Finding the optimal 'short' and 'long' windows for a moving average crossover.
Testing momentum strategies on highly volatile BTC/ETH markets.