Day trading tends to be a retail trader’s gateway into the trading world. It has a low barrier to entry and doesn’t require significant upfront capital. But that also means it’s easy for new traders to lose their hard-earned cash to the market due to technical and fundamental knowledge of how the market works.
Those who spend the time learning and honing their skills start seeing consistent gains and end up choosing to stick with day-trading. But then there are logistical barriers, such as a lack of time. As a day trader, you are entering and closing positions within the same day. This begs regular time investment during market hours, which can be difficult since most traders have a standard 9 to 5 job that provides their primary source of income.
Day trading isn't like long-term investments where you invest in a company and watch your portfolio grow slowly over the years. In day trading, minute-to-minute fluctuations of the market matter. You need to constantly monitor price moments, trends, news, and other variables that can affect markets, sectors, and specific stocks, all of which culminate into your portfolio’s health.
Logistical barriers like these have pushed day traders towards algo-trading — using algorithms and software, such as automated trading bots, to conduct automated trades. But, algo-trading isn’t without flaws.
The Problems of Algo-Trading
Algo-trading has high entry barriers that go beyond being capital intensive. For example, creating algorithms typically requires knowing programming languages, such as C++, Python, and JAVA. Users also need to access multiple sources of institutional data (usually locked behind paywalls) to test the validity of their algorithms.
Then the users need to find platforms that support backtesting and paper trading techniques to run tests for assessing the algorithm's viability. Lastly, users need to be familiar with various data science concepts to analyze test results correctly. These are just a few hurdles that those interested in algo-trading face.
Also, most people approach algo-trading with wrong expectations — like a button they can push to beat S&P 500. This is an erroneous expectation. Algo-trading’s true purpose is to give you a vessel to experiment with various automated strategies, then Live trade with the best ones at a speed and precision that manual traders cannot match.
There’s a need for algo-trading platforms that removes barriers and boosts technical accessibility for regular traders.
Breaking Equity, for example, offers just that. It is a user-friendly platform that allows users to create, backtest and paper trade with their algorithms without knowing how to code. It aims to make algo-trading technology simpler and accessible by eliminating the mentioned barriers.
Users can simply choose a strategy from its vast library or create their own. Then, they can backtest that strategy against 15 years of institutional-grade historical data and further verify their strategy’s efficacy in current market conditions through paper trading.
Once the algo-strategy’s performance gets validated, users can then Live trade with it by connecting it to their existing broker through one-click integration. Once deployed, the trading-algo will execute buy and sell actions automatically, adhering to the rules and risk-control metrics set by the user. Although automated trading traditionally comes with numerous barriers that suppress its availability in the retail community, things are changing. Day Traders can use platforms like Breaking Equity to optimize their trading activities, thereby increasing their odds of seeing profits. Still, trading requires some level of human intelligence, so solutions like trading robots shouldn’t be considered perfect.
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