Useful FinTech SaaS Products, Guides & Blogs for Quant Trading
The objective of this article is to give a comprehensive overview of useful fintech SaaS products, Apps, websites, guides, and blogs for aspiring quant traders.
The FinTech or “financial technology” has been playing an increasingly important role in financial marketplaces as digital transformation and AI-powered automation are fundamentally changing business processes and business models.
Quantitative Trading (aka quant or algorithmic trading) is a type of trading that uses quantitative analysis and mathematical/statistical models to achieve max(ROI/Risk) of assets in the stock market. Traders can use these models to develop risk-aware trading strategies that automatically execute trades based on specific market conditions.
The 6 most important SaaS categories for quant trading involve (1) Data Feed and Storage, (2) Algorithm Development and Testing, (3) Execution and Order Management, (4) Risk and Compliance, (5) AI and Machine Learning (ML), and (6) Cloud Computing and Blockchain.
Top Premium FinTech Tools
Quantpedia offers a comprehensive catalogue of premium tools for quantitative traders, including backtesting software, brokerage APIs, stock data, and training materials. One can browse more than 400 attractive trading systems together with hundreds of related academic papers.
QuantTradingApp uses an algorithm built on statistics and mathematical models to help take low risk trades as well as help newer traders navigate the stock market.
TradingView allows you to set up and restore your multi-monitor workspace without any of the limitations browsers traditionally face. Various highly interactive financial charts (indicators, strategies, profiles, patterns, etc.), stock market screeners and user-friendly scripts with technicals/financials are available.
Macroaxis the world’s leading AI-powered wealth optimization platform, trusted by fintech enthusiasts and investment professionals around the globe. The platform empowers investors to confidently manage their positions across diverse portfolios using professional and personalized state-of-the-art technology and solid portfolio theory.
Barchart provides market data and information solutions for websites and real-time desktop applications for traders, brokers and other market participants. Barchart is a leading provider of market data and services to the global financial, media, and commodity industries. The firm connects directly to numerous equities, futures, options, indices, foreign exchange and cryptocurrency markets and exchanges worldwide and provides market data, analysis, charts, trading tools and software products.
QuantFury offers trading and investing FREE of commissions and borrowing fees at real-time spot prices of NYSE, Nasdaq, CME, CBOE Europe, Binance, and Coinbase exchanges, including 1774 stocks, 48 crypto pairs, 57 ETFs, 9 index futures, 11 commodity futures, and 25 currency pairs.
a-Quant: Trading Ideas Signals App for Stocks, FX, Commodities , Indices and Cryptocurrencies based on AI.
ALGOTrader is a research group focused on the development and the execution of systematic quantitative trading strategies. AlgoTrader is a purpose-built institutional trading platform with a full suite of specialized tools to ensure best execution, even with highly fragmented liquidity.
Web Services for Quants
Quantopian builds software tools and libraries for quantitative finance. The basic idea of Quantopian is to let anyone that knows how to code in Python to write their own trading algorithm. Users have the option of a quick backtest, or a larger full backtest, and are provided the visual of portfolio performance.
QuantConnect offers a complete suite of cloud-based tools to research investment approaches, assess strategies with backtesting, then rapidly deploy to maximize returns.
QuntRocket supports multiple open-source Python backtesting and analysis libraries. Or, plug in your own custom scripts and tools thanks to QuantRocket’s modular, microservice architecture.
Backtrader represents the feature-rich Python framework for backtesting and trading. Backtrader allows you to focus on writing reusable trading strategies, indicators and analyzers instead of having to spend time building infrastructure.
- Quantified Strategies contains 200 free strategies, products & tools for trading.
- AlgoTraders is a research group focused on the development and the execution of systematic quantitative trading strategies.
- babypips helps new traders learn about the forex and crypto markets.
- Robot Wealth can help you fast-track your systematic trading portfolio and your quant trading skills.
- QuantInsti offers EPAT, the world’s first verified certification in Algorithmic Trading. Tap into the network of 350+ hiring partners located in 20+ countries to secure roles such as Quantitative Analyst, Quant Developer, and Risk Manager.
- Read more here.
Top Quant Python Libraries
Python has become the go-to language for quantitative trading due to its simplicity, flexibility, and the wealth of open-source libraries.
TA-Lib — Technical Analysis Library is widely used by trading software developers requiring to perform technical analysis of financial market data. TA-Lib includes 150+ indicators such as ADX, MACD, RSI, Stochastic, Bollinger Bands, etc.; Candlestick pattern recognition; Open-source API for C/C++, Java, Perl, Python and 100% Managed .NET.
pandas_ta is an extension of the pandas library, designed specifically for technical analysis. Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib.
yfinance is a simple and convenient library for downloading financial data from Yahoo Finance. It allows you to access historical stock prices, dividends, splits, and various financial statements with just a few lines of code. yfinance is an excellent starting point for acquiring financial data for your quantitative finance projects.
pyfolio is a performance and risk analysis library developed by Quantopian, specifically designed for analyzing and visualizing the performance of trading strategies. It provides a set of useful metrics and visualizations to better understand the risk-return profile of your strategies and compare them against various benchmarks.
QuantLib is a free/open-source C++ library for financial quantitative analysts and developers, aimed at providing a comprehensive software framework for quantitative finance. The choice of using the QuantLib Python bindings and Jupyter was due to their interactivity, which make it easier to demonstrate features, and to the fact that the platform provides out of the box excellent modules like matplotlib for graphing and pandas for data analysis.
statsmodels is a statistical modeling library that focuses on providing a wide array of statistical models, hypothesis tests, and data exploration tools. It is particularly useful for time series analysis and econometric modeling in quantitative finance.
Read more here.
Cloud Services for Quant Trading
Live Algo Trading on the Cloud — AWS
Live Algo Trading on the Cloud — Google Cloud Platform
Live Algo Trading on the Cloud — Microsoft Azure
Live algorithmic trading on the Cloud means that your trading bots can use the cloud provider’s resources to run 24/7 while being easily maintainable.
AlgoCloud — World’s first no-code cloud algo-trading platform for stocks and stock picking.
How to make an automated stock trading system with Azure
Azure Marketplace — AlgoTrader SaaS Offering
Shivam Sharma Automated Stock Trading System: With Azure, Machine Learning, Spark, Functions & Power BI(#1. Architecture)!!
Prerak Sanghvi Building a High Performance Trading System in the Cloud
Online Training Courses
Best Algorithmic Trading Courses & Certificates Online [2023]
Whether you’re just starting out or already have some experience, Coursera offers various Algorithmic Trading courses designed to fit your needs. Curated from top educational institutions and industry leaders, our selection of Algorithmic Trading courses aims to provide quality training for everyone.
Udemy: Top Algorithmic Trading Courses Online
Deploying Algorithmic Trading Strategies on the Cloud
Best Algorithmic Trading Courses (2023)
Best Algorithmic Trading Courses
60+ Algorithmic Trading Courses and Learning Tracks — Quantra
Post Graduate Program in Algorithmic Trading (PGPAT)
Algorithmic Trading Using Python — Full Course
Algorithmic Trading Workshop 2023 | Learn Algorithmic Trading [Full Course]
E-Learning Guides
Introductory Post for beginners in Algorithmic Trading
5 Best Books on Algorithmic Trading 2022
Automated Trading using Python
Harshit Tyagi Getting Starting with Algorithmic Trading with Python
Python for Algorithmic Trading + PDF
Python For Finance Tutorial: Algorithmic Trading
Krit Junsree A Beginner’s Guide to Building Your First Trading Bot in Python
Ordinary Programmer Algorithmic Trading with Python: A Beginner’s Guide
How to Get Started with Algorithmic Trading in Python
Machine Learning for Algorithmic Trading in Python: A Complete Guide — Part II
The Beginner’s Guide to Algorithmic Trading: Getting Started
Quantitative Trading Tutorial
Algorithmic Trading Course: An Introduction for Beginners
Python for Traders | A Short Introduction | Python Trading strategies
Algorithmic Trading Python 2023 — FULL TUTORIAL Beginner
Unlocking the Power of Machine Learning in Trading
Trading Network Peaks
Quant Finance Master’s Guide 2023
The chatbot and the quant: GPT shakes finance education
Algorithmic trading news and analysis articles
Articles on Algorithmic trading
Latest Stock Market Trends
- According to the latest MRFR report, the financial market is expected to be driven by the rising adoption of AT solutions. The latter can help traders to make more informed decisions and to execute trades more quickly and efficiently. Overall, AT market is going to reach USD 38.25 Billion by 2030, at a 12.35% CAGR.
- Stocks to Watch in 2023: MarketBeat Ideas
- Uncertainty is the Only Certainty for 2023 — the best choices for investors include fundamentally sound blue chip companies with sustainable dividends and share repurchase programs.
- Focus on Opportunity, Value and Dividends in 2023 — the energy sector looks like the best one to own; Financials, reflected in the Financial Select Sector SPDR Fund (NYSEARCA: XLF), also looks attractive as a group.
- Stock Market ’22 Round Up & ’23 Outlook: Zacks Strategy vs Seeking Alpha Tactics
- Energy Is Still A Strong Play In 2023
- S&P 500 2023 Outlook: Stay Alive In The First Half To Climb The Wall Of Worry In The Second
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Check out our website and articles with implemented open-source Python codes.
