Algorithmic trading is becoming highly popular among traders. It removes the risk of human error, reduces transaction costs, and can be very profitable. However, it can be a little daunting if you’re new to automated trading.
That’s why we’ve put this guide together. It’ll tell you precisely what algorithmic trading is—its pros and cons, technical requirements, and the best algorithmic trading tools. We’ll also explore some common strategies and give you an example.
What is algorithmic trading?
Algorithmic trading is automated trading that places trades for you using a computer program with pre-defined instructions. It’s also often referred to as algo-trading or black-box trading. The idea is that profits can be generated from trades much faster and more frequently than a human trader could.
The algorithm’s instructions can be based on any mathematical model and include guidelines for the price, quantity, and timing of trades. As there is no human involvement in the trade, the impact of human errors and emotions is removed from trading activities.
The Advantages of Algorithmic Trading
There are several reasons why you should look into algorithmic trading, including:
- Trade orders are placed instantly and accurately, resulting in low latency. This helps avoid significant price changes.
- Algo-trading can result in the best execution of trades at optimal prices.
- Multiple market conditions are checked automatically.
- Transaction costs are reduced.
- No risk of human error affecting trade
- Backtesting with historical and real-time data can be used to determine the viability of trading strategies.
The Disadvantages of Algorithmic Trading
Of course, algorithmic trading also has several downsides, including:
- Algorithmic trading predicts future market movements by using mathematical models and historical data. This means that black swan events (unforeseen market disruptions) can lead to losses.
- Although algo-trading’s low latency is mostly positive, it does mean that when trade execution is delayed, it could result in losses and missed opportunities.
- Large algorithmic trades can significantly impact market prices. If traders don’t adjust their trades to adapt to these changes, it can result in losses.
- Technical issues like slow internet connections can disrupt trade.
- There are complicated and often time-consuming regulations with which algo-trading has to comply.
- It can be expensive to establish and implement algo-trading systems. Plus, traders regularly have to pay fees for data feeds and software.
- Depending on your preferred trading approach, the lack of human judgment in algo-trading can be seen as a downside. It might not be the best option if your approach is more instinctive and intuitive.
What are the technical requirements needed for algorithmic trading?
To use algo-trading, you need to integrate your trading strategy into a computerized process that can place orders through a trading account. Once you’ve done this, you need to backtest it, which involves testing it on historical trades to see if it would have succeeded.
The specific technical requirements for algo-trading are:
- If you have programming knowledge, you can program the algorithm yourself. If not, you’ll need to hire programmers to do it for you. You can also use pre-made software (more on this soon).
- Network connectivity.
- Trading platforms access for placing orders
- The infrastructure and ability to perform backtesting on the system
- The system needs to be able to monitor market data feeds so it can identify opportunities.
- For backtesting, you’ll need access to historical data.
Algorithmic Trading Tools
You can use algorithmic trading software if you lack the technical requirements to program a trading algorithm and don’t want to hire programmers. The best software currently available includes:
- Kalshi
- TradeStation
- DXcharts
- Tickeron
- Build Alpha
- Trality
- Pocket Option
- FxPro
- BigShort
- Wunder Trading
- Allpips
- HaasOnline
- Coinrule
- TradingView
- UltraAlgo
- SpeedBot
- Pionex
- CryptoHawk
How To Choose the Best Algo-Trading Tool
When comparing different tools, consider the following points:
- Consider how steep the learning curve is. Is the software easy to use? Do you need coding knowledge? Make sure the platform you choose matches your comfort level and technical expertise.
- Assess how customizable and configurable the software is. This is vital for tailoring it to align with your risk tolerance and trading preferences.
- Evaluate the price and make sure it represents good value for money.
- Ensure the software has strong backtesting capabilities.
- Find a platform with a user-friendly interface.
- Identify the types of assets that are available and any ongoing fees.
Algorithmic Trading Strategies
From options trading online to swing trading, there are many different strategies traders can employ. Likewise, there are many common strategies used in algo-trading, such as:
Trend-following strategies
These are the most commonly used algo-trading strategies. They follow trends in channel breakouts, moving averages, price level movements, and related technical indicators. They don’t involve any price forecasting or predictions, so they’re the most straightforward strategies to put in place. When desirable trends are identified, trades are initiated.
The most popular trend-following strategy involves using 50 and 200-day moving averages.
Arbitrage opportunities
This strategy involves identifying dual-listed stock. Then, you buy it at a lower price in one of the markets and sell it at a higher price in a different market—the price differential results in arbitrage (risk-free profit). Algorithmic trading can make this strategy profitable by quickly identifying opportunities and placing orders efficiently.
Trading range (mean reversion)
The theory of this strategy is that the prices of assets always revert to their mean value periodically and that low and high prices are only temporary. When assets break in and out of a defined price range, the algorithm automatically places trades on them.
Mathematical model-based strategies
These strategies are based on market trends, economic theory, data, and price movements. They’re increasingly popular among traders, leading to more systematic and efficient trading within financial markets.
Some examples of mathematical model-based strategies include:
- Stochastic portfolio theory
- Relative arbitrage
- Bottom timing
- Neural networks
- Delta neutral
Volume-Weighted Average Price (VWAP)
VWAP is used to execute large orders while limiting their impact on the market. It helps traders achieve prices close to the market VWAP over set periods. It’s also regularly used as a benchmark when comparing trade executions.
Percentage of Volume (POV)
This algorithmic trading strategy is set up to send partial orders until a trade order is filled. These orders are sent according to the volume traded in the markets and the defined participation ratio.
Time Weighted Average Price (TWAP)
This strategy is used to break up large orders and then dynamically release determined smaller pieces of the order to the market. It uses evenly divided time slots between start times and end times to do so. It’s designed to limit market impact by executing orders close to the average prices between the start and end times.
Implementation shortfall
By trading off the real-time market, this strategy can reduce the execution costs of orders and take advantage of the opportunity cost of delayed execution. It decreases the targeted participation rate when the stock price moves unfavorably, and increases it when it moves favorably.
Algorithmic Trading Time Scales
Depending on your objectives and specific market conditions, algorithmic trading can operate across various time scales. The most common time scales for this type of trading include:
- High-Frequency Trading (HFT): This involves executing large numbers of trades in very short time scales. It’s aimed at capitalizing on small market inefficiencies.
- Swing Trading: This is where positions are held for several days or weeks to profit from price changes.
- Intraday Trading: Intraday traders can use algorithms to place orders in seconds. An hour-based time scale can also be implemented to execute orders.
The time scales you choose will greatly influence the design, execution, and profitability of your algorithmic trading system. So, you must adopt the time scale that best suits your risk tolerance and specific objectives.
Example of Algorithmic Trading
Let’s look at an example of a trend-following algorithmic trading strategy.
- The algorithm identifies a golden cross. This event occurs when the 50-day moving average crosses above the 200-day moving average.
- A buy signal is generated, meaning the algorithm executes a buy order for the financial instrument.
- The moving averages continue to be monitored by the algorithm.
- A sell signal emerges when the 50-day moving average falls below the 200-day moving average.
- A sell order is executed, which closes the position.
- This allows the algorithm to avoid losses during downward trends and capture profits during upward trends.
- The moving average crossovers and the selected parameters must be highly accurate to be effective.
Conclusion
Algorithmic trading utilizes tech to create a trading system that removes the risk of human error from decision-making and can generate higher profit levels than traditional human trading. Although it requires some technical expertise to program your algorithm, there are plenty of pre-made tools out there you can use instead.
The use of automated systems doesn’t negate the importance of strategic planning and thoughtful decision-making. Traders should have a clear understanding of their risk appetite, financial objectives, and the nuances of the markets they are engaging with.
Algorithmic trading is a powerful tool that has reshaped the landscape of financial markets. Whether you choose to craft your own algorithms or utilize pre-made tools, it’s essential to approach algo-trading with a comprehensive understanding of its benefits, risks, and how it fits into your broader trading strategy.
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