In this article, we will talk about what Algorithmic trading strategies are, how to identify algorithmic strategies, the best algorithmic strategies, and the common algo strategies.
Algorithmic/Algo trading is when a person uses computer codes and software to open/close trades according to set rules, e.g., points of price movement in an underlying market. Once the current market conditions match any predetermined criteria, trading algos can execute a buy or sell order on the trader’s behalf – saving the trader time by eliminating the need to scan the markets manually.
What are Algorithmic Trading Strategies?
Algorithmic trading, also called black-box trading, automated trading, or algo-trading, uses a computer program that follows a particular set of instructions to place trades. The algorithmic trade, in theory, can generate profits at a frequency that is impossible for a human trader.
The algorithmically defined sets of instructions are based on price, timing, quantity, or any mathematical model. Apart from profit opportunities for the investors/traders, algo-trading renders markets liquid and trading systematic by ruling out the impact of people emotions on trading.
How to Identify Algorithmic Trading Strategies
The first and most obvious consideration is whether you understand the strategy. As a trader, would you be able to explain the strategy perfectly, or does it require a string of caveats and a long parameter list? In addition, does the strategy have a sound, solid basis in reality? For instance, could you point to some behavioral rationale or money structure constraints that might be causing the pattern you are attempting to use?
Would this particular constraint hold up to a regime change, such as a dramatic disruption in the regulatory environment? Does the strategy depend on complex mathematical or statistical rules? Does the strategy apply to any financial time series, or is it specific to the asset class on which it is claimed to be profitable?
It would be best to consider these factors when evaluating a new trading strategy. Otherwise, you may waste significant time attempting to backtest and optimize unyieldeable strategies. Once you have understood the basic principles of the strategy, you need to crosscheck if it fits with your personality, as mentioned earlier.
Certain personality types can handle more significant drawdown periods or are willing to accept greater risk for a more significant return. Even though we, as quants, tried and eliminated cognitive bias and were able to evaluate a strategy dispassionately, biases will always find a way to creep in. Thus we need a consistent, unemotional means to assess the performance of strategies. Below is a composed list of criteria that a potential new strategy is being judged by:
- Methodology
Is the strategy momentum-based, mean-reverting, market-neutral, or directional? Does the strategy depend on complex or sophisticated statistical or machine learning techniques that are difficult to understand and require a Ph.D. in statistics to grasp? Do these techniques introduce different parameters that might lead to optimization bias? Is the strategy likely to withstand a regime change?
- Sharpe Ratio
The Sharpe ratio heuristically characterizes the risk/reward ratio of the strategy. It quantifies how much return a trader can achieve for the level of volatility endured by the equity curve. Naturally, traders need to determine the period and frequency of this volatility, and returns are measured.
- Leverage
Does the strategy require significant leverage to become profitable? Does the strategy necessarily need to use leveraged derivatives contracts (options, futures, swaps) to make a return? These leveraged contracts can have heavy volatility characteristics and can easily lead to margin calls. Do traders have the trading capital and the temperament for such volatility?
- Frequency
The frequency of the strategy is linked to the technology stack, the Sharpe ratio, and the overall level of transaction costs. All other issues are considered high-frequency strategies which require more capital, are more sophisticated, and are challenging to implement. However, assuming the backtesting engine is bug-free and sophisticated, they will often have far higher Sharpe ratios.
- Volatility
Volatility is strongly related to the “risk” of the strategy, and the Sharpe ratio characterizes this. If unhedged, higher volatility of the underlying asset classes often leads to higher equity curve volatility and smaller Sharpe ratios.
- Average Profit/Loss
Strategies have different characteristics of average profit/loss and win/lose. You can have a profitable strategy even if the number of losing trades is more than the number of winning trades. Momentum strategies tend to have this type of pattern as they rely on a small number of “big hits” to gain and be profitable. Mean-reversion strategies are different; it has a pattern where more of the trades are “winners,” but the losing trades can be severe.
- Parameters
Specific strategies require many parameters, and every extra parameter a strategy requires makes it more vulnerable to optimization bias/curve-fitting. You should target strategies with as few parameters or make sure you have enough quantities of data with which to test your strategies on.
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- Maximum Drawdown
The maximum drawdown is the overall peak-to-trough percentage drop on the strategy’s equity curve. Momentum strategies are known to suffer from periods of extended drawdowns. Many traders/investors will give up in periods of extended drawdown for the strategy. You will need to know what percentage of drawdown you can accept before you cease trading your strategy. It is a personal decision, and it must be considered carefully.
- Benchmark
Nearly all strategies are measured against some performance benchmark. The benchmark is an index that characterizes a vast sample of the underlying asset class the strategy trades in.
The Best Algorithmic Trading Strategies
Any algo trading strategy requires a profitable opportunity for good earnings or cost reduction. The following are the trading strategies used in algorithmic trading:
- Trend-Following Strategies
The most common algo trading strategies follow trends in moving averages, price level movements, channel breakouts, and related technical indicators. These strategies are the most straightforward strategies to implement through Algo trading because these strategies do not involve price forecasts or predictions. Trades/investors are initiated based on desirable trends, which are simple and easy to implement through algorithms without getting into the difficulty of predictive analysis.
- Mathematical Model-Based Strategies
Proven mathematical models, like the delta-neutral trading strategy, allow trade on a combination of options and the underlying security (Delta neutral is a portfolio strategy that consists of different positions with offsetting negative and positives deltas—a ratio comparing the change in the price of an asset, most especially marketable security, to the corresponding change in the price of its derivative—so that the overall delta of the assets in question totals zero.)
- VWAP (Volume-Weighted Average Price)
Volume-weighted average price (VWAP) strategy breaks a large order and releases a dynamically determined more minor part of the order to the market using stock-specific historical volume profiles. This strategy aims to execute the order close to the VWAP (Volume-Weighted Average Price).
- TWAP (Time Weighted Average Price)
The TWAP (time-weighted average price) strategy breaks a large order and releases dynamically determined smaller parts of the order to the market using evenly divided time slots between a start/end time. This strategy aims to execute the order close to the average price between the start time and end times, thereby minimizing market impact.
- POV (Percentage of Volume)
Until the trade order is filled, this algorithm sends partial orders according to the defined participation ratio and the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and decreases or increases this participation rate when the stock price reaches user-defined levels.
- Implementation Shortfall
The implementation shortfall strategy’s purpose is to minimize an order’s execution cost by trading off the real-time market, thereby saving the cost of the order and gaining from the opportunity cost of delayed execution. This strategy increases the targeted participation rate when the stock price moves upwards and decreases it when it moves adversely.
Most common types of Algorithmic trading strategies
Algorithmic trading strategies can be used in various ways to achieve profit. Some are more efficient than others, but all can be effective if executed correctly.
- Arbitrage Opportunities
Buying a dual-listed stock at a lesser price in one market and simultaneously selling it at a costlier price in another market offers the price differential as arbitrage or risk-free profit. The same operation can be replicated for future vs. stocks instruments as price differentials exist continuously. Implementing an algorithm to identify this price differential and placing the orders efficiently allows good and profitable opportunities.
- Index Fund Rebalancing
Index funds have defined rebalancing periods to bring holdings to par with respective benchmark indices. This rebalancing period creates profitable opportunities for algo traders, who capitalize on expected trades that offer profits of 21 to 80 basis points depending on the number of stocks in the index fund just before the rebalancing. These trades are initiated via Algo trading systems for timely execution and the best prices.
- Trading Range (Mean Reversion)
The mean reversion strategy is a strategy that is based on the concept that an asset’s low and high prices are a temporary phenomenon that periodically reverts to its mean value. Defining and identifying a price range and implementing an algorithm based on this trade allows trades to be automatically placed when the price of an asset breaks out and is in its defined range.
5 Best Algorithmic Trading Software
If you’re looking for an algorithm that will help you make profits and achieve your trading goals, these are the five best options.
- Coin Rule
- Mudrex
- TradeStation
- Ticker
- Trality
FAQS
Q. How much do algorithmic traders make?
A. The salaries of Algo Traders in the US range from $20,075 to $535,865, with a median salary of $96,856. The middle 57% of Algo Traders make between $96,856 and $243,045, with the top 86% making $535,865.
Q. Is algorithmic trading more profitable?
A. Algo trading isn’t just profitable but also increases the chances of becoming a profitable trader/investor. It has to do with the fact that all strategies that have been trading have been validated on historical data, with the superior order execution that’s offered by a trading computer
Q. How much money do you need for algorithmic trading?
A. A trader needs 20 times its yearly expenses to be a full-time trader. However, the minimum amount could be as low as $300 if you want to learn and test your ideas.
Q. How difficult is algorithmic trading?
A. While algorithmic trading may seem easy, it isn’t easy to set up and maintain. It requires the algorithmic trader to do a lot of market research to find trading edges, code algos to take advantage of the trading edges, backtest the strategies, test them for robustness, and launch them to trade.
Q. Is Algo trading better than manual trading?
A. There are several reasons why Algo trading is better than manual trading. Algo trading performs complex calculations and does not miss out on trading opportunities, and it faces no emotional conflicts that arise when making trading decisions.
Q. What is the success rate of algorithmic trading?
A. Algo trading has a 97 percent success rate.
Q. How effective is algorithmic trading?
A. The primary benefits of algo trading are that it provides the “best execution” of trades because it minimizes the human element and can trade multiple assets and markets far more efficiently than a human trader.
Conclusion
Algorithmic trading strategies are a common type of trading method used in the stock market. They involve using computer algorithms to make predictions about future prices, and then buying and selling stocks based on those predictions. This can make more money than if you tried to do traditional trading methods alone.
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