The thrill of trading often comes with an anxiety-inducing reality: what happens while you sleep? What if a significant market event occurs and you fail to act quickly enough to limit losses on your open positions? A good algorithmic trading strategy, especially one designed for low latency trading, can help mitigate these fears. Algorithmic trading strategies run on autopilot, executing trades for you according to preprogrammed rules. This article will help you confidently implement algorithmic trading strategies to maximize profits, minimize risk, and simplify decision-making in financial markets.
QuantVPS’s trading VPS is a valuable tool for achieving your trading goals with algorithmic trading strategies. This virtual server provides a stable and secure environment for running your automated trading systems without interruptions, allowing you to take full advantage of your strategy.
What is Algorithmic Trading?
Algorithmic trading, or algo trading, occurs when computer algorithms, not humans, execute trades based on predetermined rules. Think of it as a team of automated trading systems that never sleep, endlessly analyzing market trends and making trades in the blink of an eye.
The algorithms can be trained to manage all sorts of trading. Quick trading and highly liquid markets can make this tool more effective, so it is more commonly seen in fast-moving markets such as:
- Stocks
- Foreign exchange
- Cryptocurrencies
- Derivatives
Low or nonexistent transaction fees make profiting easier with rapid, automatically executed trades, so the algorithms typically aim for low-cost opportunities.
The Future of Investing
A tweak here and there can adapt the same trading algorithms to slower-moving markets such as bonds or real estate contracts (those quick-thinking computers get around). Over time, these systems have grown increasingly sophisticated, utilizing artificial intelligence (AI) techniques like machine learning and deep learning.
Some even use large language models (LLMs) similar to OpenAI’s ChatGPT, analyzing financial news and social media chatter to make trading decisions. In theory, a more detailed set of real-world variables can make the algorithm more practical.
A High-Speed Investment Strategy
Algorithmic trading is an investment strategy that resembles a 100-meter dash more than The Fool’s usual approach of steady long-term ownership of top-shelf quality companies. But even though you might not plan on lacing up for an algorithmic trading sprint, understanding it is critical in the modern investing world. After all, large portions of today’s stock market rely directly on this tool.
The Power of Algorithms in Modern Trading
Most algorithms employ quantitative analysis, executing trades when the asset’s trading follows a specific pattern. It’s helpful to give the computer access to some bottomless pockets, to the point where its automatically executed trades can control the real-time price action to some degree. Even without that price-moving advantage, the millisecond reaction time of a computerized trader can turn a profit even from a relatively quiet market with little price movement.
An Inside Look at Algorithmic Trading
To understand how a quantitative stock fund uses algorithmic trading, let’s imagine a situation with a fictional stock called the Intergalactic Trading Company, which has the ticker “SPAACE.” Our quant fund has developed a complex model trained on vast historical market data.
Predicting Price Movements with Algorithmic Models
This model predicts, with a certain degree of probability, that when the trading volume of SPAACE shares crosses a certain threshold with positive price momentum, the price will rise significantly in the next few minutes before it corrects back down. That’s how the stock has behaved many times in the past, and it looks likely to do it repeatedly, maybe many times in a single day or even in five minutes.
Identifying a Trading Opportunity
SPAACE is a volatile little ticker! One fine Monday morning, as the opening bell rings on Wall Street, the fund’s trading algorithm, affectionately called QuantBot, springs into action. It monitors the trading volume of SPAACE, never taking its digital eye off the proverbial ball. At 10:03 AM, QuantBot notices that the trading volume of SPAACE starts to spike.
Executing the Trade
QuantBot quickly cross-checks the other criteria outlined in its model. The share price is moving upward, and a quick AI-powered sentiment analysis of social media posts about SPAACE shows increasing investor enthusiasm. All the stars align, and the algorithm decides to make its move. It doesn’t have a choice; this is exactly what the program was looking for! Faster than a blink, QuantBot purchases a substantial number of SPAACE shares.
Capitalizing on the Trend
The share price starts climbing in this brief window due to the uptick in volume on top of already-positive market sentiment. Remember, this happens within minutes or seconds, or maybe fractions of a second in some cases. At 10:07 AM, QuantBot notes that the price momentum is slowing down and stabilizing. Following its pre-defined rules, it swiftly sells off the SPAACE shares it bought, locking in a tidy profit.
A Profitable Strategy
Our QuantBot pal has made a profitable trade in this scenario by identifying a quick market trend using data and algorithmic precision. It took advantage of the price surge it helped create, bailing out before the artificial price trend turned back down. This is one of the many ways a quantitative fund can aim to make money using algorithmic trades.
Note that the Intergalactic Trading Company’s business results have almost nothing to do with this process. Algorithmic trading sessions like these play out daily, with or without real-world news, to inspire market action.
The Double-Edged Sword of Algorithmic Trading
The show can go on as long as people (or other algorithms with different trading criteria) are ready to buy what your bot is selling and sell what it’s buying. It sounds easy when you lay it out like this, but many of the ideas involved run counter to the ideas of fair markets and investor transparency that we hold dear at The Fool.
The Challenges and Costs of Algorithmic Trading
The algorithm is not a magic wand. Many variables and risks are involved, and you need high-powered computers and plenty of investable funds to implement this trading strategy effectively. Even the most sophisticated trading algorithms often lose money on individual trades.
The Limits of Algorithmic Trading
You must know when to hold ’em, fold ’em, etc. This is not a game for the typical individual investor but a specialized arena for the algorithmically adept and financially fortified. The rest of us are better off following the patient long-term investing tenets of Warren Buffett and Benjamin Graham.
The Relevance for Buy-and-Hold Investors
As buy-and-hold investors, we might wonder why we need to understand the lightning-fast world of algorithmic trading. Successful long-term investing is better served by patience, foresight, and staying the course, right? Even from the sidelines, you should know how algorithmic trading influences the markets.
The High-Stakes Game of Algorithmic Trading
These algorithms can affect stock prices and market volatility, creating ripples that eventually touch our portfolios. It’s important to remember that these trading algorithms are designed for the financial equivalent of bullet chess, with one hand on the clock and where fractions of a second mark the difference between winners and losers. That’s not the slow and steady investing game we humans are used to, and not necessarily one we should attempt to emulate.
Proceed with Caution, Dear Investor
Should you be tempted to dip your toes into algo trading? As a seasoned long-term investor, I recommend caution. Algo trading, especially the kind driven by advanced AI, is a complex field that requires a unique set of skills in:
- Programming
- Data analysis
- Finance
The Risks of High-Speed Trading
The fast pace of algo trading could lead to quick gains, but remember that rapid losses can pile up just as swiftly, especially in volatile market conditions. For us, it might be like trying to sprint in a marathon. You’re looking at exhaustion and potential injury (financially speaking) more quickly than sticking with a slow and steady pace.
A Lesson in Investing
You and I are not computerized hares, moving more like the inexorable tortoise of Aesop’s classic fable. And there is nothing wrong with that since a more systematic approach suits human investors better. Warren Buffett made his billions without leaning on digital high-speed trades.
Algorithmic trading funds like Citadel and Renaissance Technologies may have made multibillionaires out of Jim Simons and Ken Griffin, but even they can’t hold a candle to Buffett’s more methodical wealth-building acumen.
Related Reading
- How to Automate Trading
- Introduction to Algorithmic Trading
- Trading Risk Management
- How to Set Up Algorithmic Trading
23 Algorithmic Trading Strategies
1. Momentum: The Power of Price Action
Momentum trading has been popular for decades. The idea is simple: assets that have been rising in price tend to keep rising, while those that have been falling tend to keep falling. An algorithmic trading strategy based on momentum might buy a stock when its price reaches a certain level and then sell it later when it peaks according to the algorithm’s criteria. The higher the price action a system can detect, the better.
2. Trend Following: Riding the Wave
Trend-following strategies also focus on price action but take a more systematic approach. These strategies identify an established trend, either upward or downward, and then look for opportunities to buy or sell while the trend continues. Like momentum strategies, trend-following algorithms can help keep traders on the right side of the market and avoid false reversals.
3. Risk-On/Risk-Off: Analyzing Market Sentiment
Under risk-on/risk-off strategies, algorithms monitor changes in investor risk appetite and automatically execute trades based on their findings. For example, during periods of economic expansion, risk appetite tends to increase, and algorithms will look to identify opportunities in higher-risk assets, such as small-cap stocks or commodities.
Conversely, during economic uncertainty, algorithms will detect shifting sentiment and begin liquidating positions in riskier assets to bolster returns in safer investments, like government bonds.
4. Inverse Volatility: Hedging Against Market Risk
An inverse volatility strategy focuses on exchange-traded funds (ETFs) that move in the opposite direction of market volatility. When market volatility rises, these funds lose value, and algorithmic trading can help detect when this occurs to automatically execute buy orders to hedge against losses in a trading portfolio. Conversely, the strategy can help traders sell these funds when market volatility decreases to lock in profits.
5. Black Swan Catchers: Preparing for the Unexpected
Black swan events are unpredictable occurrences that can devastate financial markets. An algorithmic trading strategy focusing on black swan events looks to capitalize on the increased market volatility after such an event occurs.
While the specific details of these strategies will vary, they generally look to identify trading opportunities in speculative assets, like options, that tend to increase substantially in value during periods of heightened uncertainty.
6. Index Fund Rebalancing: Profiting Off the Big Guys
Index funds are designed to track the performance of a specific benchmark index. These funds periodically rebalance their portfolios to maintain accurate exposure to the underlying assets in the index. When this occurs, algorithmic traders can capitalize on it. The trades this rebalancing brings can offer profits of anywhere between 20 and 80 basis points, depending on how many stocks are in the index fund before rebalancing.
7. Mean Reversion: Betting on the Average
Mean reversion strategies are based on the temporal nature of high and low asset prices. The concept is that assets will periodically revert to their average (or mean) value. The challenge, therefore, is to identify when such a mean reversion is about to take place and act accordingly. If a reversion is poised to drive the price higher, it’s time to buy; likewise, if reversions are going to drop the price, it’s time to sell.
Algorithmic trading is the perfect tool for identifying and defining a price range for an asset. Whenever the price of that asset breaks out of its defined range and indicates a mean reversion, the algorithm can be configured to place the appropriate trades automatically.
8. Market Timing: Waiting for the Perfect Moment
Market timing strategy is about waiting until the perfect moment to buy or sell an asset. This strategy can be hit or miss, as an investor can wait for an asset to hit what they perceive to be an all-time low, only to see the price drop even further after investing in it. Likewise, investors can miss out on profit if they sell an asset when a perceived high hit, only to watch its value climb higher after their sale.
Algorithmic Trading for Improved Market Timing
Algorithmic trading can help smooth out these issues with market timing. By analyzing current market trends and comparing them against historical activity, algorithms can help determine whether an investor’s timing choices are accurate. While still not perfect, using algorithmic trading in this way can significantly reduce false starts.
9. Arbitrage: Exploiting Price Differences
Arbitrage investment occurs when an investor simultaneously purchases and sells the same asset in different markets. The goal of arbitrage is to profit from small, often tiny, differences in the listed price of the asset. Arbitrage is most effective when exploiting short-lived price variations in the same or nearly identical assets in different forms or markets.
Arbitrage requires pinpoint accuracy in buy and sell orders to maximize profit. This is where algorithmic trading can be beneficial. Algorithms can monitor relevant markets for timing and help eliminate issues related to risk and transaction costs quicker than a human trader can.
10. Direct Market Access: Speed and Efficiency
Direct Market Access (DMA) eliminates the need for intermediaries between buyers and sellers by connecting them directly with liquidity providers and exchanges. This trading approach allows for:
- More rapid order execution
- Reduced trading expenses
- Greater transparency
With DMA, traders control the order book and make transactions at particular prices by entering orders at the appropriate times. Institutional investors and high-frequency traders frequently use this algo due to its efficiency and quickness.
11. Percentage of Volume (POV): Trading with the Crowd
Percentage of Volume (POV) algorithms execute orders based on the trading volume of a particular asset. 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 increases or decreases this participation rate when the stock price reaches user-defined levels.
12. Implementation Shortfall: Minimizing Market Impact
The implementation shortfall strategy aims to minimize the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when it moves adversely.
13. Market Sentiment Trading Strategy: Riding the Emotional Waves
Market sentiment algorithms involve analyzing investor sentiment and mood to make trading decisions. Traders use sentiment indicators, social media analysis, and news sentiment to gauge the market’s emotional state.
Gauging Market Mood
Positive sentiment suggests bullish moves, while negative sentiment indicates bearish movements. Sentiment analysis can complement other trading strategies by providing additional context and insights into the prevailing market sentiment.
It’s important to note that market sentiment can be subjective and may not always reflect the true market direction. Therefore, forex traders should use sentiment analysis with other technical and fundamental analysis tools for more accurate decision-making.
14. Machine Learning Trading Strategy: Adapting to Change
The use of machine learning and artificial intelligence (AI) for algorithmic trading in the forex market is becoming increasingly popular. It helps forex traders create more nuanced and responsive trading models. Algorithms trained by machine learning can go through mountains of:
- Data
- Pick out fine trends
- Optimize investment strategies based on past results
The Adaptive Power of AI in Trading
Automatic learning from new data allows AI-powered algorithmic trading systems to refine their decision-making gradually. These algorithms may employ neural networks, support vector machines, and other AI methods to detect market tendencies and produce trading signals. Because of their flexibility and adaptability, machine-learning AI trading systems are well-suited for the ever-evolving financial markets.
15. Market Making Strategy: The Liquidity Provider
Traders or brokerages who act as market makers are responsible for maintaining the market’s liquidity by constantly quoting buy and sell prices. They earn money on the spread between the buying and selling prices, known as the bid-ask spread. Even if there is no immediate buyer or seller, they buy or sell at the prices they have quoted.
This makes trading more accessible by reducing the gap between the bid and the ask price. Because of the possibility of exposure to market volatility and unexpected price moves, market-making strategy calls for cutting-edge technology, such as using:
- Advanced algorithms
- Appropriate risk management
16. Auto Hedging Trading Strategy: Protecting Investments
Auto-hedging is a risk management method involving equivalent trades to cancel out any possible losses in a position. If a trader’s position is vulnerable to large negative price changes, an auto-hedging algorithm will immediately arrange a hedge trade to protect the investment.
Traders anticipate that their portfolios will be safer from the effects of rapid market volatility by doing so. Institutions and trading firms with substantial market exposure frequently use auto hedging. The technique relies on constant market tracking and the speed with which one can place hedging bets.
17. News-Based Trading Strategy: Capitalizing on the Reaction
In news-based trading, trading decisions follow the market’s reaction to news and economic developments. When major news is released, traders often rush, which can result in price changes.
News-based traders use algorithms to decipher news headlines, economic data releases, and other events that can move markets. When specific parameters are met, the system automatically opens positions. Notably, market volatility and slippage around major news events pose a risk to traders who rely on news to make money.
18. Index Fund Rebalancing: Profiting Off the Big Guys
Index funds periodically adjust their portfolios to align with their benchmark index. With index fund rebalancing, you’d attempt to anticipate these adjustments and position your trades accordingly. The strategy would consider potential market movements due to large-scale buying or selling by index funds.
19. Market Timing: Analyzing Indicators for Optimal Trade Execution
Market timing strategies would focus on helping you analyze various indicators and models to determine optimal entry and exit points for trades. This approach aims to enhance returns by enabling informed decisions around the timing of trades based on market conditions. It requires a thorough understanding of market behavior and an ability to adapt to changes quickly.
20. Stealth Trading Strategy: Keeping a Low Profile
The stealth trading approach aims to make as little noise as possible when making trades. Due to the size of the order, market prices may shift in a way that is unfavorable to the trader when a large order is placed. Stealth trading algorithms split massive orders into smaller, less apparent pieces to avoid substantially impacting the market. Traders lessen their exposure to slippage and price manipulation by conducting transactions in private.
21. High-Frequency Trading (HFT) Strategy: Speed Kills
High-frequency trading involves executing many trades quickly to take advantage of small price movements. HFT firms use ultra-fast computers and low-latency connections to gain an edge in the market. HFT strategies exploit little price discrepancies, often holding positions for a fraction of a second to a few seconds.
HFT has revolutionized trading, but it also faces criticism for potential market instability and the advantages it provides to firms with the fastest technology. Although this algorithmic strategy is becoming increasingly popular, brokerage and prop firms mostly do not allow traders to use High-frequency trading.
22. Iceberging Trading Strategy: The Best Way to Hide Your Orders
The Iceberg algorithmic strategy involves placing a huge order in the market without disclosing the complete order size. A portion of the order is placed in the market for all to see, but the remainder (the substantial part) remains unseen and needs to be executed.
When one section is complete, the next section is disclosed to process the order. Institutional traders, or those dealing with high order sizes, frequently adopt this method to mitigate the potential for adverse price movements brought on by the simultaneous execution of numerous orders.
23. Volume-Weighted Average Price (VWAP) Strategy: A Good Benchmark
To calculate the average price of a forex pair over a given period, traders employ an algorithmic trading approach known as the Volume Weighted Average Price (VWAP). The volume-weighted average price (VWAP) is determined by adding up the prices of all trades throughout the specified time, multiplying by the total volume of those trades, and then dividing by that amount.
This approach aims to give investors an indicator of the market average price against which they may judge the quality of their trades’ prices. Traders widely use VWAP to predict market movement and identify probable support and resistance levels.
Related Reading
- Best Automated Trading Platform
- Automated Trading System
- High-Frequency Trading Algorithm
- Automated Trading Strategies
- Futures Trading Algorithms
How to Pick the Best Algorithmic Trading Strategy
Get Real with Yourself About Trading Psychology
The most important step to excelling at algorithmic trading isn’t about researching strategies or backtesting data—it’s about understanding yourself. Whether discretionary or algorithmic, successful trading requires an honest evaluation of your personality and preferences. Expect to be emotionally tested as you trade.
The Importance of Discipline in Algorithmic Trading
Algorithmic trading uses a computer program to execute trades on your behalf using pre-set rules. You must resist the urge to interfere when the system runs, even if the strategy incurs losses. Many traders need to recognize this psychological hurdle before starting with algorithmic trading.
Eventually, they let their emotions take over and ruin a potentially profitable trading system. Remember that many strategies that show promise in backtests will incur losses when traded live. To be successful, you must allow the system to operate without interference. This will require you to assess its performance over time objectively.
Consider Your Available Time for Trading
Before choosing an algorithmic trading strategy, consider how much time you must devote to trading. Do you have a full-time job? A part-time job? What are your daily responsibilities? These questions are critical to determining the frequency of the strategy you should pursue. For instance, if you have a full-time job, a high-frequency intraday futures strategy will only be appropriate once you automate it).
Your time constraints will also dictate the strategy’s methodology. Suppose the approach you’re interested in is frequently traded and relies on expensive news feeds (like a Bloomberg terminal). In that case, you must be realistic about your ability to run it successfully while at the office!
Assess Your Trading Capital
Most experts agree that a minimum starting capital of $50,000 is ideal for algorithmic trading. If you plan to trade with less than this, you may need help to cover transaction costs that can be high for frequently traded strategies. Starting with less than $10,000 is particularly risky.
You will likely need to restrict yourself to low-frequency strategies trading one or two assets. Interactive Brokers—the friendliest broker to those with programming skills due to its API—has a retail account minimum of $10,000.
Evaluate Your Programming Skills
The importance of programming skills in algorithmic trading cannot be overstated. Knowledge of programming languages such as C++, Java, C#, Python, or R will enable you to create the end-to-end data storage, backtest engine, and execution system of your algorithmic trading strategy.
This has several advantages, chief of which is the ability to be completely aware of all aspects of the trading infrastructure. It also allows you to explore the higher-frequency strategies as you will fully control your technology stack.
The Trade-Off Between DIY and Outsourcing
While this means that you can test your software and eliminate bugs, it also means more time spent coding up infrastructure and less on implementing strategies, at least in the earlier part of your algo trading career. You may be comfortable trading in Excel or MATLAB and can outsource the development of other components. But I would not recommend this, particularly for those trading at high frequency.
Establish Your Goals for Algorithmic Trading
What do you hope to achieve by algorithmic trading? Are you interested in a regular income source where you hope to draw earnings from your trading account? Or, are you interested in a long-term capital gain and can afford to trade without the need to draw down funds?
Income dependence will dictate the frequency of your strategy. More regular income withdrawals require a higher frequency trading strategy with less volatility (i.e., a higher Sharpe ratio). Long-term traders can afford a more sedate trading frequency.
Don’t Be Misled by the Get-Rich-Quick Mentality
Forget any notions you might have about becoming extremely wealthy in a short space of time! Algorithmic trading is NOT a get-rich-quick scheme; if anything, it can be a become-poor-quick scheme. It takes significant discipline, research, diligence, and patience to be successful at algorithmic trading. It can take months, if not years, to generate consistent profitability.
Sourcing Algorithmic Trading Ideas
Despite common perceptions to the contrary, it is pretty straightforward to locate profitable trading strategies in the public domain. Trading ideas have been less readily available than they are today.
Numerous resources offer thousands of trading strategies to inspire and refine your ideas, including:
- Academic finance journals
- Pre-print servers
- Trading blogs
- Trading forums
- Weekly trading magazines
- Specialist texts
Evaluate Trading Strategies
The first and arguably most obvious consideration is whether you understand the strategy. Can you explain the strategy concisely, or does it require a string of caveats and endless parameter lists? In addition, does the strategy have a good, solid basis in reality? For instance, could you point to some behavioral rationale or fund structure constraint that might be causing the pattern(s) you are attempting to exploit?
Would this constraint withstand a regime change, such as a dramatic regulatory environment disruption? Does the strategy rely on complex statistical or mathematical rules? Does it apply to any financial time series, or is it specific to the asset class in which it is claimed to be profitable?
You should constantly consider these factors when evaluating new trading methods; otherwise, you may waste significant time attempting to backtest and optimize unprofitable strategies.
Matching Strategy to Personality
Once you have determined that you understand the strategy’s basic principles, you must decide whether it fits your personality above profile. This is more specific a consideration than it sounds! Strategies will differ substantially in their performance characteristics.
The Human Element in Algorithmic Trading
Certain personality types can handle more significant periods of drawdown or are willing to accept greater risk for larger returns. Although we, as quants, try to eliminate as much cognitive bias as possible and should be able to evaluate a strategy dispassionately, biases will always creep in. Thus, we need a consistent, unemotional means to assess strategies’ performance.
Here is the list of criteria that I judge a potential new strategy:
Methodology
Is the strategy momentum based, mean-reverting, market-neutral, directional? Does the strategy rely on sophisticated (or complex!) statistical or machine learning techniques that are hard to understand and require a PhD in statistics to grasp?
Do these techniques introduce many parameters which might lead to optimization bias? Is the strategy likely to withstand a regime change (i.e., potential new regulation of financial markets)?
Sharpe Ratio
The Sharpe ratio heuristically characterizes the reward/risk ratio of the strategy. It quantifies how much return you can achieve for the level of volatility endured by the equity curve. Naturally, we need to determine the period and frequency over which these returns and volatility (i.e., standard deviation) are measured. A higher frequency strategy will require a greater sampling rate of standard deviation but a shorter overall measurement period, for instance.
Leverage
Does the strategy require significant leverage to be profitable? Does the strategy necessitate using leveraged derivatives contracts (futures, options, swaps) to make a return? These leveraged contracts can have heavy volatility and thus can easily lead to margin calls. Do you have the trading capital and the temperament for such fluctuations?
Frequency
The frequency of the strategy is intimately linked to your technology stack (and thus technological expertise), the Sharpe ratio, and the overall level of transaction costs. All other issues considered, higher-frequency strategies require more capital, are more sophisticated and are harder to implement. But assuming your backtesting engine is refined and bug-free, it will often have far higher Sharpe ratios.
Volatility
Volatility is strongly related to the strategy’s “risk.” The Sharpe ratio characterizes this. If unhedged, higher volatility of the underlying asset classes often leads to higher volatility in the equity curve and, thus, smaller Sharpe ratios.
Of course, I assume that the positive volatility is approximately equal to the negative volatility. Some strategies may have greater downside volatility. You need to be aware of these attributes.
Win/Loss, Average Profit/Loss
Strategies will differ in their characteristics of win/loss and average profit/loss. One can have a very profitable strategy, even if the number of losing trades exceeds the number of winning trades.
Momentum strategies tend to have this pattern as they rely on a few “big hits” to be profitable. Mean-reversion strategy tends to have opposing profiles, where more trades are “winners,” but the losing trades can be quite severe.
Maximum Drawdown
The maximum drawdown is the most significant overall peak-to-trough percentage drop on the strategy’s equity curve. Momentum strategies are well known to suffer from periods of extended drawdowns (due to a string of many incremental losing trades).
Many traders will give up during periods of extended drawdown, even if historical testing has suggested this is “business as usual” for the strategy. You must determine what percentage of drawdown (and over what period) you can accept before you cease trading your strategy. This is a highly personal decision that must be considered carefully.
Capacity/Liquidity
At the retail level, you must be trading in a highly illiquid instrument (like a small-cap stock) to avoid having to concern yourself greatly with strategy capacity. Capacity determines the scalability of the strategy to further capital. Many of the more considerable hedge funds suffer from significant capacity problems as their strategies increase in capital allocation.
Parameters
Specific strategies (especially those in the machine learning community) require many parameters. Every extra parameter makes a strategy more vulnerable to optimization bias (“curve-fitting”). Try to target a strategy with as few parameters as possible or ensure you have sufficient data to test it.
Benchmark
Nearly all strategies (unless characterized as absolute return) are measured against some performance benchmark. The benchmark is usually an index representing a large sample of the underlying asset class the strategy trades in. If the strategy trades large-cap US equities, then the S & P500 is a natural benchmark to measure your strategy against.
You will hear the terms “alpha” and “beta” applied to this strategy. We will discuss these coefficients in depth in later articles.
Notice that we have yet to discuss the actual returns of the strategy. Why is this? In isolation, the returns provide us with limited information as to the effectiveness of the approach. They don’t give you an insight into:
- Leverage
- Volatility
- Benchmarks
- Capital requirements
Thus, strategies are rarely judged solely on their returns. Always consider a plan’s risk attributes before examining its returns.
At this stage, many of the strategies found in your pipeline will be rejected out of hand since they won’t:
- Meet your capital requirements
- Leverage constraints
- Maximum drawdown tolerance
- Volatility preferences
The remaining strategies can now be considered for backtesting. Still, before this is possible, one final rejection criterion must be considered—that of available historical data on which to test these strategies.
Obtaining Historical Data
Nowadays, the breadth of the technical requirements across asset classes for historical data storage is substantial. The buy-side (funds) and sell-side (investment banks) invest heavily in their technical infrastructure to remain competitive. It is imperative to consider its importance. In particular, we are interested in timeliness, accuracy, and storage requirements.
Obtaining and Storing Historical Data
I will now outline the basics of obtaining historical data and how to store it. Unfortunately, this is a profound and technical topic, so I won’t be able to explain everything in this article. Still, I will be writing much more about this in the future as my prior industry experience in the financial industry was chiefly concerned with economic data acquisition, storage and access.
In the previous section, we set up a strategy pipeline that allowed us to reject certain strategies based on our personal rejection criteria. In this section, we will filter more strategies based on our preferences for obtaining historical data.
The chief considerations (especially at the retail practitioner level) are the data costs, storage requirements, and your level of technical expertise. We also need to discuss the different types of available data and the considerations each type of data will impose on us.
Let’s begin by discussing the types of data available and the key issues we will need to think about:
Fundamental Data
This includes data about macroeconomic trends, such as interest rates, inflation figures, corporate actions (dividends, stock splits), SEC filings, corporate accounts, earnings figures, crop reports, meteorological data, etc. This data is often used to value companies or other assets fundamentally, i.e., via some means of expected future cash flows. It does not include stock price series.
Some fundamental data is freely available from government websites, while other long-term historical fundamental data can be costly. Storage requirements are often relatively small unless thousands of companies are being studied simultaneously.
News Data
News data is often qualitative. It consists of articles, blog posts, microblog posts “tweets”, and editorial. Machine learning techniques such as classifiers are often used to interpret sentiment. This data is also often freely available or cheap via subscription to media outlets. The newer NoSQL document storage databases are designed to store this unstructured, qualitative data.
Asset Price Data
This is the traditional data domain of the quant. It consists of a time series of asset prices. Equities (stocks), fixed-income products (bonds), commodities, and foreign exchange prices belong to this class. Daily historical data is often straightforward for simpler asset classes, such as equities. Nevertheless, the data can become expensive once accuracy and cleanliness are included and statistical biases are removed.
In addition, time series data often possesses significant storage requirements, especially when intraday data is considered.
Financial Instruments
Equities, bonds, futures, and the more exotic derivative options have very different characteristics and parameters. Thus, no “one size fits all” database structure can accommodate them. Significant care must be given to designing and implementing database structures for various financial instruments. In future articles, we will discuss the situation at length when building a securities master database.
Frequency
The higher the frequency of the data, the greater the costs and storage requirements. For low-frequency strategies, daily data is often sufficient. Obtaining tick-level data and even historical copies of particular trading exchange order book data might be necessary for high-frequency strategies.
Implementing a storage engine for this data type is very technologically intensive and only suitable for those with a strong programming/technical background.
Benchmarks
The strategies described above are often compared to a benchmark, manifesting as an additional financial time series. This is usually a national stock benchmark for equities, such as the S&P500 index (US) or FTSE100 (UK). Comparing against a basket of bonds or fixed-income products is useful for a fixed-income fund.
Another widely accepted benchmark is the “risk-free rate” (i.e., appropriate interest rate). All asset class categories possess a favored benchmark, so if you wish to attract external interest in your strategy, you will need to research this based on your particular strategy.
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QuantVPS provides a reliable, speed-optimized environment for traders to run their automated strategies continuously and efficiently.
Speed Kills! Why Latency is So Important for Algorithmic Trading
Speed is critical in algorithmic trading. Even a few milliseconds can mean the difference between profit and loss. Choosing a trading VPS with ultra-low latency will ensure your automated strategies execute quickly, helping you achieve your goals.