Several decades ago, the financial world witnessed the rise of discount brokers, who offered low-frills trading services in exchange for reduced commissions. With the birth of the world wide web came online brokers like E*Trade, who promised even lower commissions for traders and investors who were willing to manage their own trading. In 2015, the investment world was stunned once more with the launch of Robinhood, which enticed 15 million young traders with the offer of zero commissions and no minimum fee accounts. Now virtually all of the investment firms offer their customers commission-free trading.
The latest, and even more significant change, in our opinion, is the rise of what we call, "independent trading platforms" (ITPs). These are software companies 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. Their business models are typically monthly software as a subscription service, rather than commissions or trading fees. The most prominent example of an ITP is TradingView which attracts 1.2MM unique visitors each month. TradingView started as a charting and research platform, but in the past year has established API hooks to more than a dozen broker-dealers.
ITPs enjoy the tremendous cost advantage of not having to build and maintain brokerage back end systems, which are highly regulated and complex. Many, including TradingView (at my last visit), have also avoided filing as RIAs (Registered Investment Advisors), as they avoid making suggestions to their users on what assets to trade or hold.
In the past year, it's been off to the races, as nearly a dozen new ITPs have launched, with names like Streetbeat, Composer, PlutoHQ, Predict.ai, and last but not least, our entry, MachineTrader.io. Most of these platforms offer their users the ability to create algorithmic trading scripts, and many describe themselves as low-code/no-code, although in actuality they offer customizable pre-constructed templates.
From what we've seen, we, at MachineTrader, are the only one that offer a highly functional, yet highly flexible visual framework, that allows non-programmers to connect "nodes" (pre-built code functions and scripts), and create powerful, functional trading algorithms. MachineTrader also provides its users with a robust reporting platform, with extensive time series charting, news feeds, sentiment analysis, and economic reports. View the FREE demo account at http://demo.machinetrader.io (login: demo password: demo) or signup for a 30-day FREE trail at http://www.machinetrader.io.
While there are sure to be a variety of implementations as time goes on, users can expect tons of innovation as ITPs compete to attract the nearly ten million active traders worldwide.
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 trading is a trading strategy where traders buy and sell securities within the same trading day taking advantage of short-term price fluctuations in various assets to make quick profits. Day traders aim to capitalize on small price movements by entering and exiting positions multiple times throughout the day.
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.
You're a newbie to trading and you open your Robinhood account and begin to actively trade fractional shares of Tesla (TSLA). Your first dozen trades of the day work great as you pick the best moments to buy and sell. All of a sudden your account is flagged with a 'pattern day trader' warning.
