We are pleased to announce the launch of a free 90-minute training course on Udemy, designed to teach students how to trade stocks and options algorithmically without writing code. It provides a comprehensive introduction to Node-RED, a low-code, event-driven programming tool for automating tasks and integrating APIs. Node-RED is an open-source, flow-based development tool for visual programming. It was developed by IBM and is used for wiring together hardware devices, APIs, and online services in an intuitive, drag-and-drop interface. It is particularly popular for IoT applications, automation, and API integrations.
Students will be given access to the proprietary, password-protected trading platform, MachineTrader™, under the terms of a 30-day free trial. MachineTrader™ was designed to serve the needs of serious traders who want to automate their trading but who lack the full stack programming skills to build their own platforms. Each MachineTrader™ trading subscription includes a hosted website which can expand to your unique requirements. This also means that all of your data and strategies are completely private - your algorithms remain proprietary to you!
They will also be instructed in opening a paper trading account with the registered broker/dealer, Alpaca Markets.
Course Objectives
By the end of this course, participants will:
• Understand the fundamentals of Node-RED, including its interface and core concepts.
• Develop automation workflows using built-in and third-party nodes.
• Connect Node-RED with IoT devices, APIs, and cloud services.
• Learn best practices for debugging, security, and deployment.
• Implement real-world projects such as home automation and API integration.
Click here to sign up for the course: Algorithmic Trading for Non Programmers
This course provides a detailed walkthrough on using the MachineTrader backtester to test and refine trading algorithms using historical data. The backtester enables users to simulate algorithm performance and make necessary adjustments for improved trading results.
We are pleased to announce the launch of a free 90-minute training course on Udemy, designed to teach students how to trade stocks and options algorithmically without writing code.
Trading activity in the new Bitcoin ETFs fell rapidly yesterday. While several of the ETFs made it into the top 5 most heavily traded ETFs on Friday, interest on Tuesday, when trading resumed after the MLK holiday, the bloom was off the rose.
The long-awaited Bitcoin ETFs debuted in yesterday's trading, making quite a splash. Three Bitcoin ETFs were among the five most actively traded ETFs: GBTC, IBIT, and FBTC, all trading more than 10 million shares.
Day traders employ a variety of strategies in the quest to make profitable trades. This is a list of the more common ones:
Quantitative trading, also known as algorithmic trading or algo trading, is a method of trading financial assets using advanced mathematical and statistical models. It involves the use of computer algorithms to execute trades based on predefined rules and strategies. This approach to trading has gained significant popularity in recent years, with many institutional investors and hedge funds adopting it as an integral part of their investment strategies.
Swing trading is a popular trading strategy that involves taking advantage of short-term price movements or "swings" within a larger trend. Swing traders aim to capture gains by entering and exiting positions over a period of days or weeks, rather than holding positions for months or years like long-term investors.
The main objective of algorithmic trading is to maximize profits by taking advantage of short-term market inefficiencies. It aims to exploit price discrepancies, liquidity imbalances, and other temporary market conditions that might arise within fractions of a second. By automating the trading process, algorithmic trading eliminates human emotions and biases, enabling faster and more efficient execution of trades.
This strategy creates a portfolio of QQQ (Invesco Trust Series 1) that is traded in correspondence with logic defined by the Moving Average Convergence/Divergence (MACD) lagging indicator. The MACD strategy is a commonly employed algorithmic trading strategy, and can be calculated using Exponential Moving Averages (EMAs) by hand using the following formulas.
This strategy creates a portfolio of IWB (iShares Russell 1000 ETF) that is bought based on the recent news sentiments. The state of the news being reported has an incredible effect on the stock market, as popular reporting agencies can directly increase/decrease people’s optimism about the economy, leading to a snowball effect, of sorts.
Building custom ETFs using MachineTrader™ visual programming toolkit is easy to do and often an effective way to enhance your portfolio. While technically these aren’t algorithmic trading strategies, you can easily add flows that will optimize the ETF based on performance of the individual assets.
The latest significant change in the investment world is the rise of what we call, "independent trading platforms" (ITPs). These are software platforms whose user-friendly web and mobile applications provide all of the functionality of trading platforms, but pass the actual trade executions off, through an API, to the registered broker-dealer of the user's choice.
Monitoring minute-by-minute (or second-by-second) changes in time series charts of prices is a pretty tedious task. While a number of trading platforms offer off-the-shelf algorithms or bots for automating strategies, we've found that it's hard to make money using an algo that other traders are employing, or worse, other traders have programmed to work against.
