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.
While the performance of the Bitcoin ETFs was underwhelming, with an average loss for the day of 6% compared with stock market averages which were down about 0.2%, which is what you'd expect when the price of Bitcoin drops - these ETFs appear to be the real deal in terms of trading activity.
Momentum traders are looking for volatility and the Bitcoin ETFs delivered. We calculated the standard deviation of one-minute price changes through the day and the Bitcoin ETFs led the pack.
The standard deviation of price changes for BRRR was 0.171, but as a percentage of its price, it's 1.42% for a one minute price change provides plenty of action - nearly 10x the volatility of SPY.
There was also a surprising variation in the daily price changes for the ETFs which should be minimal since they are tracking the same asset, Bitcoin (I assume that's BTCUSD). If these differences persist and they exceed the bid/ask spreads, there may be an opportunity for pairs trading. GBTC only posted a 4.68% loss for the day compared with DEFI, which lost a whopping 8.15%. Whether it will be possible to arb the difference in prices between the various ETFs will be a function of the bid/ask spreads (which didn't look too bad based on spot checking yesterday) and whatever trading costs you incur with your broker-dealer.
It's not yet possible to short the Bitcoin ETFs so momentum traders will have to limit their interest to one side of the trading. Overall, not a bad debut for active trading.
The author is CEO of MachineTrader.io, a low code/node code software platform that can be used by non programmers to automate their trading.
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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.
