The Ultimate Guide to Trend-Following Trading Bot Systems
1. INTRODUCTION
“The trend is your friend” is a well-known saying among traders, and for a good reason. It implies that going with the flow – that is, trading in line with the current market trend – often leads to better outcomes than opposing it. The belief behind this phrase is based on the tendency of market trends to continue for a considerable time, driven by momentum and investor behaviour. Therefore, capitalising on these trends is typically less risky and more straightforward than trying to predict when they will shift. Does the thought of consistently benefiting from these robust market trends interest you? If so, a trend-following trading bot system might just be what you’re looking for. And you’ve landed in the right place – we at Oxido Solutions are seasoned experts in trend-following trading bot systems and related areas, including algorithmic trading, crypto bots, crypto legal matters, and middleware software.
In this blog, we’re primed to provide you with a thorough understanding of the following topics of trend-following trading bot systems:
- Definition Trend-Following Systems
- Comparison With Other Trading Systems
- Key Components
- Types Of Trend-Following Systems
- Advantages Of Trend-Following Systems
- Disadvantages Of Trend-Following Systems
- Oxido Solution’s Trend-Following System
- The Future Of Trend-Following Systems
- Final Thoughts
2. DEFINITION TREND-FOLLOWING BOT SYSTEMS
Trend-following trading bot systems are trading systems that empower users to implement financial market strategies, aiming to profit from the consistent direction of an asset’s price. Designed to identify and trade in the direction of a trend until it shifts, these rule-based systems make decisions based on preset parameters. Depending on the type of asset involved, these systems can pinpoint trading opportunities during both market upswings and downturns.
3. COMPARISON WITH OTHER TRADING SYSTEMS
Trend-following systems contrast sharply with four other popular trading systems: mean-reversion systems, high-frequency trading systems, grid Systems and dca systems.
3.1. Comparing Trend-following with Mean-Reversion Systems
Mean-reversion systems operate on the premise that prices or returns gravitate towards their average over time. Trades are made when prices significantly deviate from this average, expecting them to revert to the mean. Conversely, trend-following systems exploit price movements in one direction and function on the belief that an established trend is more likely to persist than reverse. Thus, while mean-reversion systems profit from price variations from the mean, trend-following trading bot systems profit from sustained trends.
3.2. Trend-following versus High-Frequency Trading (HFT)
HFT is a type of algorithmic trading known for its speed and high turnover rates, indicating that many assets are bought and sold in a very short span. It’s also characterized by its large order-to-trade ratios, meaning that while many orders (like buy or sell requests) are placed, only a few result in an actual trade. This approach is all about rapidity, often executing trades almost instantaneously, literally in milliseconds! In contrast, trend-following systems usually require longer periods to spot and track market trends.
3.3. Trend-following versus Grid Trading Systems
Contrary to trend-following trading bot strategies that focus on market direction, grid trading systems use market volatility and price fluctuations within a certain range to generate profits. While trend-following systems begin a trade with the start of a new trend and end when the trend seems to be concluding, grid trading systems implement multiple trades at pre-set levels, irrespective of trend direction, anticipating price bounces within these levels. Trend-following strategies follow the “cut your losses short, let your profits run” principle, potentially incurring more losses but capturing substantial profits from significant trends. Meanwhile, grid systems target smaller profits from many trades, risking major losses if the market strongly trends out of its range, especially without a stop-loss.
3.4. Comparing Trend-following with Dollar-Cost Averaging Systems (DCA)
DCA is a passive approach that mitigates market volatility by spreading asset purchases over time, making it suitable for long-term investors seeking wealth preservation and gradual growth. On the other hand, trend-following is an active strategy that capitalizes on market momentum to generate profits, requiring more market understanding and a greater tolerance for potential drawdowns. While DCA eliminates the need for constant market monitoring, trend-following demands discipline to follow the trend and not second-guess signals during adverse market phases.
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4. KEY COMPONENTS
Trend-following trading bot systems come in many shapes and sizes and can be tailored and implemented to cater to different trading needs. For instance, while some systems support trading in both upward and downward trends, others are designed to exclusively support either ‘long-only’ trades or speculation on falling prices. Additionally, these systems can vary in terms of the asset class they focus on, the time frame they operate within, and the data they use as inputs. However, all trend-following systems share six essential components. These are:
4.1. Trading bot Rules
Every trend-following trading bot system is designed based on rules. These rules may always remain the same, but they can also change over time, especially if the system is equipped with artificial intelligence. Rules are crucial for any trend-following system for various reasons:
4.1.1. Discipline
Trading can often be led by emotions, causing traders to make impulsive decisions driven by fear or greed. Rules assist traders in maintaining discipline, sticking to a pre-set plan irrespective of short-term market fluctuations.
4.1.2. Consistency
Rules ensure that traders apply the same standards to every trade, which can make outcomes more predictable in the long run. This uniformity also simplifies the process of evaluating a trading strategy’s performance and making necessary modifications.
4.1.3. Objectivity
Once rules are set, trading decisions become less subjective. This can aid in eliminating cognitive biases that could otherwise adversely impact trading decisions.
4.1.4. Automation
Transparent, objective rules enable the automation of trading decisions. Automated trading systems can execute trades more rapidly and accurately than humans and can operate around the clock.
4.1.5. Performance Measurement and Improvement
A well-defined set of rules simplifies the process of evaluating the effectiveness of a trading strategy. You can analyze which aspects are functioning and which are not, thus providing you an opportunity to refine and enhance your strategy over time.
4.1.1.1. Analysis methods for establishing rules
The rules of a trend-following trading bot system can be derived from various analysis methods that establish these trading rules. The most common methods are technical analysis, fundamental analysis, and sentiment analysis.
In technical analysis, the determination of the trend direction and its corresponding trading opportunities are based on technical indicators such as moving averages and momentum indicators. On the other hand, alerts from fundamental trend-following systems are generated based on macroeconomic indicators like unemployment and consumer confidence, as well as microeconomic indicators such as a specific company’s annual results.
A trading system based on sentiment analysis uses news articles, social media posts, and other text-based sources to assess the overall sentiment or mood of investors. This sentiment, which can be bullish (positive), bearish (negative), or neutral, then informs predictions about future market trends. For example, a mainly bullish sentiment might signal a potential upward trend in the market, whereas a largely bearish sentiment could indicate a potential downward trend.
These different methods of generating alerts are often combined within trend-following systems to enhance the robustness of the predictions and mitigate the risk of false signals.
4.2. Risk Management
While rules define the conditions under which a trade will be initiated, managed, and closed, risk management, on the other hand, involves defining the amount of risk acceptable for each trade and for the portfolio as a whole. Risk management is crucial in trend-following systems to help a trader preserve capital and ensure that he can stay in the market over the long term. Each trend-following system might have different risk management techniques depending on the trader’s style, risk tolerance, and market conditions.
Key components of risk management in trend-following trading bot systems include:
4.2.1. Position Sizing
This involves determining the amount of capital to invest in each trade. The size of the position can depend on various factors such as the volatility of the asset, the trader’s risk tolerance, and the total capital available for trading. In general, traders should avoid risking too much capital on a single trade to minimize potential losses.
Example of Position Sizing:
Let’s say you have a trading account with a balance of $10,000. You’ve set a rule for yourself that you won’t risk more than 2% of your total account balance on any single trade. This means the maximum amount you’re willing to lose on any trade is $200 (which is 2% of $10,000).
You’re interested in buying the stock PayPal, which is currently trading at $50. After analyzing the stock, you decide that you’ll set your stop-loss order at $48. This means you’re willing to tolerate a drop of $2 per share before you sell the stock to prevent further losses.
To determine the size of your position (i.e., how many shares of PayPal you should buy), you’d do the following calculation:
Max Loss Per Trade / Loss Per Share = Number of Shares
$200 / $2 = 100 shares
Therefore, based on your risk tolerance and the specific stop-loss for PayPal, you should buy 100 shares. This ensures that if the stock hits your stop-loss, you’ll lose no more than $200, adhering to your 2% risk rule.
This method of position sizing ensures that you manage your risk effectively, regardless of the size of your trading account or the price of the security you’re trading.
4.2.2. Stop-Loss and Take-Profit Orders
These are orders that close a trade once a certain price level is reached. A stop-loss order is designed to limit potential losses if the market moves against the trader’s position, whereas a take-profit order locks in profits once the asset reaches a predetermined price level. A distinction can be made between fixed stop-loss and take-profit orders and dynamic stop-loss and take-profit orders:
4.2.3. Fixed Stop-Loss
This is set at a specific price level away from the entry price. For instance, if you buy a stock at $100, you might set a fixed stop-loss at $95. This means if the stock falls to $95 or below, it will automatically be sold, limiting your loss.
4.2.4. Fixed Take-Profit
This is also set at a specific price level. If you buy a stock at $100 and set a take-profit at $110, your position will automatically be sold when the price reaches $110, ensuring your profit.
4.2.5. Dynamic Stop-Loss
Instead of a fixed price level, a dynamic or trailing stop-loss moves with the market price. It’s set at a certain percentage or dollar amount away from the market price. If the market price moves favorably, the stop-loss will adjust, but if the market price moves against your trade, the stop stays in place. This allows traders to lock in profits as the market moves in their favor while still providing downside protection.
4.2.6. Dynamic Take-Profit
This is less common than dynamic stop-loss. However, the concept is similar. The take-profit level adjusts based on some criteria (like a percentage move) to potentially capture more profit if the asset continues to move favorably, but it will lock in profit once the asset starts moving in the opposite direction by a set criterion.
In summary, while fixed orders remain static and don’t change unless manually adjusted, dynamic or trailing orders adjust to market movements, which can be especially useful in protecting profits in fast-moving markets.
4.2.7.Volatility Management
Given that market volatility can impact trend strength, it’s crucial for trend-followers to monitor and respond to changes in volatility. This could involve reducing position sizes during periods of high volatility to limit risk or in periods of sideways price action when the volatility is too low
4.2.8. Diversification
This involves diversifying investments across different financial assets to minimize the risk tied to any one asset. In a trend-following system, diversification could mean trading across multiple assets or markets to ensure the system’s performance isn’t solely dependent on the trend of a single asset. Additionally, within individual assets, one might choose to diversify by trading on various time frames.
Here is a hypothetical example of a trend-following trading trading bot system incorporating these principles of diversification:
Assets Selection
– The system trades in a mix of assets: stocks, commodities, currencies, and bonds.
– From each category, it selects three prominent representatives.
– Stocks: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN)
– Commodities: Gold, Crude Oil, Silver
– Currencies: EUR/USD, GBP/USD, USD/JPY
– Bonds: 10-year U.S. Treasury, 10-year German Bund, 10-year UK Gilt
Time Frame Diversification
– Each asset is traded on three different time frames to capture trends of varying durations:
– Short-term (daily charts)
– Medium-term (weekly charts)
– Long-term (monthly charts)
Trading Rules
– The system uses moving averages as its primary trend indicators. This is a common tool used in technical analysis to help smooth out price data and identify trends in financial markets. They provide a simple way to visualize the average price of an asset over a specified number of periods.
– A simple crossover system is implemented:
– Buy Signal: When the short-term moving average (e.g., 50-day MA) crosses above the longer-term moving average (e.g., 200-day MA).
– Sell Signal: When the short-term moving average crosses below the longer-term moving average.
Position Sizing
– The system uses a fixed percentage risk model, risking only 1% of the total account balance on any single trade.
Stop Loss & Take Profit
– Dynamic stop-loss levels are set based on the Average True Range (ATR) of each asset, a technical analysis indicator that measures the volatility of an asset’s price. Originally developed by J. Welles Wilder in the 1970s, the ATR provides an average of the “true ranges” for a given number of periods
– Take profit levels are dynamically determined based on the desired risk-to-reward ratio.
Why This System Emphasizes Diversification
– By trading across multiple asset classes, the system reduces its dependency on any one market. For instance, when stocks might be in a downtrend, commodities or currencies might be in an uptrend.
– Using different time frames helps the system capture both short-term and long-term trends. For example, while the daily chart might show a downtrend for Apple, the monthly chart could still indicate a long-term uptrend.
– This diversified approach ensures that the system isn’t overly exposed to sudden reversals in any single market or timeframe.
This hypothetical trend-following system illustrates how a trend-following strategy can be diversified across assets and time frames to minimize risks and enhance potential returns.
4.2.9. Regular Review and Adjustment
Effective risk management involves regularly reviewing trading performance and market conditions, and adjusting the trading strategy accordingly. This could involve modifying the trading algorithms or system rules in response to changing market trends or the trader’s risk profile.
SIDEWAYS FILTER
4.3. Trading Bot Data
To implement rules and risk management effectively, data is crucial. As such, data serves as the backbone of any trend-following trading bot system. Without access to high-quality data and the expertise to analyze it accurately, running a system that generates valuable buying and selling opportunities for trend-following trading strategies would be unfeasible. With technological advancements such as AI trading and quantum computing, the importance of data in financial markets is continually growing. In this section, we will delve into the different types of data that can be used in trend-following trading systems. In broad terms, two types of data serve as input for trend-following trading systems: quantitative and qualitative data.
4.3.1. Quantitative data
Quantitative data refers to numerical information that can be measured and analyzed statistically. This type of data is central to trend-following trading systems, as they use algorithms to identify patterns and trends in historical and live market data.
4.3.1.1. Historical price data
Historical price data refers to the past recorded prices of a particular financial asset. It is a record of an asset’s price history over a certain period of time.
This data usually includes the opening price (the price at which the first trade occurred during a specified period), the closing price (the final trading price of the period), the highest price (the maximum price reached during the period), and the lowest price (the minimum price during that period). This set of data is often referred to as OHLC data.
The historical price data plays a critical role in trend-following trading systems as it helps traders and algorithms to identify patterns and trends in price movements. By analyzing past price movements and trends, traders aim to predict future price movements and make profitable trades accordingly.
Trend-following trading bot strategies often rely on technical analysis of historical price data, using various statistical tools and indicators like moving averages, trend lines, and momentum indicators to identify and follow a trend. The assumption here is that historical trends, once established, are likely to continue into the future.
4.3.1.2. Real-time price data
Real-time price data, as it relates to trend-following trading systems, refers to the live, up-to-the-moment prices of a particular financial asset being traded on the market. This data is continuously updated and reflects the most recent prices at which an asset is being bought and sold.
In the context of a trend-following trading system, real-time price data is vital for two main reasons:
A) Trend Identification
While historical price data is used to identify existing or potential trends, real-time price data is necessary to confirm those trends and make trading decisions accordingly. For instance, if the historical data suggests an upward trend, but the real-time data shows a sudden price drop, the trend-following system may interpret this as a possible end to the trend and potentially signal an exit from the trade.
B)Trade Execution
Real-time price data is needed for executing trades at the most optimal moments. When the trading bot system identifies a trend and decides to enter or exit a trade, it uses real-time price data to execute the trading bot strategy. This ensures that the trade is made at the current market price, which is particularly important in fast-moving markets where prices can change rapidly.
4.3.1.3. Historical trading volume
Historical trading volume refers to the recorded past quantity of a particular financial asset that has been traded over a specific period of time. This data includes the number of shares, contracts, or lots that have been bought and sold, providing valuable insight into the asset’s liquidity and investor interest.
In the context of a trend-following trading system, historical trading volume plays several crucial roles:
A. Trend Confirmation
While the price direction is the main factor in identifying a trend, volume can serve as a secondary measure to confirm the trend. Generally, an increasing trading volume in the direction of the trend (rising volume during an uptrend or falling volume during a downtrend) can be viewed as a strong confirmation of the trend. If the volume is not supporting the price trend, it could be a warning sign that the trend may be weakening.
B. Market Strength
Historical trading volume can be used to measure the strength or weakness of a market. High trading volumes often indicate strong investor interest and a more active market, which can lead to more significant price movements and potential trading opportunities. On the other hand, low trading volumes might suggest a lack of investor interest or a less active market.
C. Liquidity Assessment
Trading volume data can help measure the liquidity of a market or a particular asset. High trading volumes often suggest high liquidity, which allows traders to enter and exit positions more easily without significantly affecting the price.
4.3.1.4. Real-time trading volume
Real-time trading volume refers to the live, up-to-the-minute measure of the total quantity of a specific financial asset being bought and sold during a specific trading session or time interval. This live data provides immediate insights into the current trading activity and market liquidity for that particular asset.
Real-time trading volume serves several key roles:
A. Instant Trend Confirmation
Similar to historical trading volume, real-time volume can help confirm the validity of a trend. If there is an uptick in volume as a price is moving in a particular direction, this often signals a strong trend. On the other hand,, if a trend is accompanied by a decline in volume, it may suggest that the trend is losing momentum.
B. Immediate Market Strength Evaluation
Real-time trading volume allows traders to instantly evaluate the current market strength or weakness. High trading volumes typically indicate strong market activity and could lead to larger price movements. Low volumes might suggest a lack of current market activity.
C. Current Liquidity Assessment
Real-time trading volume data helps traders to evaluate the current liquidity of a market or a specific asset. Assets with high trading volumes are generally more liquid, meaning positions can be entered and exited with less price slippage.
D. Risk Management
By observing real-time trading volumes, traders can assess the level of risk they’re willing to take. If the trading volume is too low, they might choose to avoid entering the market due to the risk of price manipulation or slippage. Alternatively, a high trading volume might be more appealing as it suggests higher liquidity and lower slippage risk.
In short, real-time trading volume is a critical component of trend-following trading systems, aiding in instant trend confirmation, immediate market strength evaluation, liquidity assessment, and effective risk management.
4.3.1.5. Order book data
Order book data refers to a real-time, continually updated list of buy and sell orders for a specific financial asset. Each order within the order book lists the number of units being bid (buy orders) or offered (sell orders) and the price the buyer or seller is willing to accept.
In trend-following trading systems, order book data serves several key purposes:
A. Market Depth Analysis
Order book data provides insight into the market depth, which points to the market’s ability to sustain relatively large orders without impacting the price of a security. By analyzing this, traders can understand how robust or weak a trend is.
B. Price Discovery
It helps in price discovery as it shows where demand (buy orders) and supply (sell orders) for a given asset is located. This information can be used to assess potential future price movements and thus align trades with the trend direction.
C. Liquidity Assessment
Order book data gives an indication of liquidity. A ‘thick’ order book with many orders close to the current price often indicates high liquidity, meaning trades can be executed without significant price slippage. Conversely, a ‘thin’ order book could suggest lower liquidity and a higher risk of slippage.
D. Sentiment Analysis
Traders can get a sense of market sentiment by observing the balance between buy and sell orders. A predominance of one type could indicate the direction in which the market is leaning.
However, it’s important to note that while order book data can provide valuable information, it has its limitations in trend-following strategies. Order book data is inherently ‘noisy’ and rapidly changing, and thus can be difficult to incorporate into longer-term trend-following systems. Also, it’s susceptible to various forms of manipulation, such as ‘spoofing’, where large orders are placed to create a false impression of demand or supply, and then withdrawn.
Despite these challenges, when used wisely, order book data can enhance the robustness of a trend-following trading bot system, providing real-time insights into market depth, price levels, liquidity, and trader sentiment.
4.3.1.6. Volatility data
In the context of trend-following trading systems, volatility data is a key factor that can significantly impact the system’s performance. Volatility is a statistical measure of the dispersion of returns for a given asset or market index. It is most commonly characterized through the standard deviation or variance between returns from that same asset or market index. In simpler terms, volatility represents how large the price changes are for a particular asset.
Volatility data can be crucial for trend-following trading systems for several reasons:
A. Trend Identification
High volatility often accompanies new trends. If an asset price starts moving with increased volatility, it could signify the beginning of a new trend. This would be a signal for a trend-following system to potentially open a new position.
B. Position Sizing
Volatility is commonly used in determining the size of the position that a trend-following system might take on a given trade. Generally, the higher the volatility, the smaller the position size, to account for the greater risk.
C. Risk Management
It can also be a crucial component of risk management. Trend-following systems need to take into account the volatility of an asset when setting stop losses and take profit levels. Generally, the higher the volatility, the wider these levels need to be to avoid being stopped out by normal market noise.
D. Trading Bot Strategy Adjustment
Depending on the level of volatility, a trend-following system may need to adjust its bot strategy. For example, in low volatility environments, a trend-following system may need to be more patient and wait for longer to capture profitable trends.
Remember, while high volatility can offer increased opportunity for profit due to larger price swings, it can also mean higher risk as prices can swing against your position just as quickly. Therefore, understanding and properly managing volatility is an essential part of running a trend-following trading system.
4.3.1.7. Trading fees
Trading fees data represents the costs incurred when executing trades on a trading platform. These fees are an integral part of any trading system because they can significantly impact the profitability of trades. Trading fees depend on the exchange and asset class being considered. Specifically, crypto derivatives and traditional assets like stock CFDs have varying trading fees.
A. Trading fees traditional assets
Here in a overview of the trading fees on traditional assets:
i. Transaction Costs
Every time a trade is placed, whether it is a buy or sell order, there may be a transaction fee associated with it. These fees are usually a small percentage of the total trade value, and they are charged by the brokerage or exchange platform where the trade is executed. It’s essential to account for these fees when calculating the potential profit or loss of a trade.
ii. Spread Costs
The spread is the difference between the bid (buy) price and the ask (sell) price of an asset. The spread itself is a cost because traders must overcome the spread to make a profit on a trade. For instance, if the spread is wide, the asset’s price needs to move more significantly in the trader’s favor to cover the spread cost and realize a profit.
iii. Financing or Swap Costs
If a trader uses leverage (borrowed capital to increase potential return on investment), there may be financing or swap costs. These costs are interest payments made on the leveraged amount and are typically charged daily.
iv. Slippage Costs
Slippage occurs when the price at which a trade is executed does not match the expected price. This discrepancy is usually due to market volatility or low liquidity. Slippage can be seen as an additional cost because it can reduce the profitability of trades.
In trend-following trading systems, these fees are even more critical because these systems often rely on making many trades that aim to capture small, consistent profits. High trading fees can significantly wipe out these small profits. Therefore, understanding and managing trading fees is a crucial part of operating a successful trend-following trading system. This data can help traders choose the most cost-effective platforms and adjust their strategies to minimize trading costs.
B. Trading fees cryptocurrency perpetual futures
Trading cryptocurrency perpetual futures with crypto bots also involves trading fees, but the structure of these fees can be somewhat different compared to traditional asset classes like stocks. Here are some of the key considerations:
i. Maker and Taker Fees
Crypto exchanges typically charge different fees depending on whether you’re a “maker” or a “taker”. A maker is someone who adds liquidity to the market by placing a limit order that isn’t immediately filled. A taker removes liquidity by placing an order that gets filled right away (like a market order). Makers usually pay lower fees than takers because they help to increase the exchange’s trading volume and liquidity.
ii. Funding Rate
In perpetual futures contracts, there’s an additional fee called the “funding rate”. Unlike traditional futures contracts, perpetual futures don’t have an expiry date. To keep the price of the perpetual future contract in line with the underlying asset’s spot price, exchanges use a mechanism called “funding”. If the funding rate is positive, long positions pay short positions. In contrast, if the funding rate is negative, short positions pay long positions. The funding rate is typically updated every few hours.
iii. Liquidation Fees
If a crypto bot trader’s position gets liquidated because they couldn’t meet the margin requirements, some exchanges may charge a liquidation fee.
iv. Withdrawal Fees
Exchanges may charge a fee to withdraw your funds. This fee can vary depending on the type of crypto asset and the current state of the blockchain network.
When trading cryptocurrency perpetual futures, it’s crucial to be aware of all these fees, as they can significantly affect your profitability, especially in a trend-following trading system where multiple positions may be opened and closed over a relatively short period.
4.3.1.8. Technical indicators data
Technical indicators data stands for the information used in statistical analysis techniques employed by traders to forecast future price movements based on historical prices and volume data. They are mathematical calculations that are visually represented in most charting platforms and are often used as a foundation for trading decisions within a trend-following trading system.
Here are a few categories of technical indicators that trend-following systems might use:
A. Trend Indicators
As the name suggests, trend indicators help traders identify the presence and direction of a trend. The most commonly used trend indicators include Moving Averages (MA), Moving Average Convergence Divergence (MACD), and the Average Directional Index (ADX).
B. Momentum Indicators
These indicators help traders to gauge the strength or speed at which the price of an asset is moving. Examples include the Relative Strength Index (RSI), Stochastic Oscillator, and Rate of Change (ROC).
C. Volatility Indicators
Volatility indicators measure the rate of price movements, regardless of the direction. They help traders to understand the dynamics of price fluctuations. Examples include Bollinger Bands and the Average True Range (ATR).
D. Volume Indicators
Volume indicators are used to understand the strength of price movements. High volume often signifies strong price moves and is seen as a confirmation of the trend. Examples include the On Balance Volume (OBV) and Chaikin Money Flow (CMF).
The specific technical indicators a trend-following trading bot system uses will depend on the parameters of the strategy. For example, a system might use moving averages to identify the direction of a trend, and then use volume indicators to confirm the strength of the trend.
It’s important to note that while technical indicators can provide valuable insights, they are not foolproof and should be used in tandem with other data points and risk management strategies. No single indicator can predict market movements with 100% accuracy, and different indicators may sometimes give conflicting signals. Therefore, traders often use multiple indicators and look for alignment among their signals to increase the probability of success.
4.3.1.9. Fundamental data
Fundamental quantitative data implies the numerical data related to the financial and economic aspects of a traded asset. While trend-following trading systems are primarily based on technical data (price and volume), they may also incorporate certain fundamental quantitative data to strengthen the trading signals and mitigate risks.
Here are some examples of fundamental quantitative data:
A. Financial Statements
For stocks, these might include a company’s balance sheet, income statement, and cash flow statement. These reports give traders insights into a company’s performance and financial health, which could indirectly affect its stock price trends.
B. Economic Indicators
These include macroeconomic data such as GDP, employment figures, interest rates, and inflation rates. For example, forex traders often pay close attention to these indicators as they can have significant impacts on currency trends.
C. Market Data
This includes data like market capitalization, dividend yield, P/E ratio, and other such metrics. For example, a significant change in a company’s market capitalization can signal a trend shift.
D. Earnings Reports
These are particularly important for stocks. An earnings report that significantly exceeds or misses expectations can result in a strong price trend.
E. News and Events
While not numerical, important news and events are often quantified and incorporated into algorithmic trading systems. This could include political events, natural disasters, product launches, and more.
Incorporating fundamental quantitative data into a trend-following trading system can help traders identify when the broader market or economic conditions might be changing in a way that could affect their current trends. While a pure trend-following system might ignore these fundamental aspects, some traders believe that keeping an eye on these factors can give them an edge.
Remember, however, that trend-following trading bot systems primarily depend on technical analysis. Fundamental indicators are generally used to inform and reinforce the trading signals provided by the technical analysis, not to replace it. Fundamental data tends to be more useful for longer-term trend analysis rather than short-term trading signals, as it often takes time for changes in fundamental data to be reflected in asset prices.
DATA LAB
4.3.2. Qualitative data
Compared to quantitative data, qualitative data is less structured and includes information that is more subjective. Qualitative data is often an overlooked, yet essential, element of every trend-following trading system for a variety of reasons.
A. Market Sentiment
Qualitative data helps in assessing market sentiment, which can influence market trends. It includes news reports, investor sentiment data, and subjective reviews of economic conditions.
B. Contextual Understanding
Trading involves a lot of nuances and interpretation. For example, a company announcement about entering a new market could potentially trigger an upward price trend. These kinds of news are qualitative data, and it’s crucial for a trader to interpret this news correctly.
C. Risk Management
Understanding qualitative factors, such as geopolitical risks or changes in company management, can help in better risk management by foreseeing potential sudden changes in trends that purely quantitative models might not capture.
D. Augment Quantitative Data
Qualitative data can help in understanding the reasons behind the numbers, i.e., why a particular trend is happening. It can add depth to the quantitative analysis and provide a more holistic view of the market dynamics.
While qualitative data plays an essential role, its subjective nature makes it more challenging to analyze than quantitative data. This is where artificial intelligence methods can come into play, offering methods like machine learning and natural language processing (NLP) to analyze text-based qualitative data systematically and at scale.
4.4. Strategy coding editor
If the rules and risk management parameters of the system have been defined and the necessary data is available, it’s time for the technical realization of a trend-following trading strategy. This can be achieved using existing plug-and-play strategies or by developing a custom strategy. This customization can be done using the strategy coding editor.
The strategy coding editor is a built-in feature within the trend-following trading system that enables developers and traders to write, edit, and implement their own specialized trading strategies. It provides a flexible and customizable environment that allows users to directly encode their trading rules and strategies. The strategy coding editor should support commonly used programming languages in algorithmic trading such as Python or C++, and support features that aid coding. These may include syntax highlighting, error detection, and auto-completion. With a strategy coding editor, traders can create unique strategies that are specifically tailored to align with their individual trading style and objectives.
4.5. Testing Suite
A comprehensive testing suite is an essential part of any trend-following trading system. The testing suite is where a trader validates their trading strategies before deploying them in the live market, and it generally includes at least three critical components: backtesting, forward testing and stress testing.
A. Backtesting
Backtesting is the process of testing a trading strategy using historical data to see how it would have performed in the past. It’s an essential part of the development and refinement of any trend-following trading system. The objective of backtesting is to evaluate the performance and risk characteristics of the trading strategy under various market conditions, and to identify and correct any issues or inefficiencies.
During a backtest, the trading rules and risk management parameters of the system are applied to the historical data, and the system generates simulated trades as if it were trading in real time. The results of the backtest provide a hypothetical performance history that can be analyzed in detail. Metrics like total return, average return, volatility, maximum drawdown, sortino ratio and others are typically evaluated.
While backtesting is a powerful tool, it’s important to note that past performance is not always indicative of future results. It is possible for a strategy to perform well in backtesting but poorly in live trading due to factors like overfitting, where the strategy is too closely tailored to the historical data and performs poorly when exposed to new data.
B. Forward Testing
Forward testing, also known as paper trading, is the process of testing the trading strategy in a simulated, live market environment using real-time data but without risking real capital. This helps in validating the performance of the strategy under current market conditions and allows the trader to make adjustments if needed before deploying the strategy with real capital.
Both backtesting and forward testing are crucial for the effective development and validation of a trend-following trading strategy. These tools allow traders to refine their strategies, identify potential issues, and gain confidence in their system before they risk real money in live trading.
C. Stress Testing
Stress testing is a vital tool used in trend-following trading systems to assess their robustness against extreme market conditions. Its objective is to evaluate how the system might fare during sudden market shifts, ensuring it can handle adverse scenarios without incurring significant losses. To perform stress testing, traders create hypothetical adverse market scenarios, such as rapid trend reversals or major geopolitical events, to simulate potential system performance. For instance, they might evaluate the system’s response to unexpected economic events like significant interest rate changes. It’s essential to recognize that stress testing can’t predict all potential scenarios; its goal is to identify vulnerabilities. After testing, traders might make adjustments to their system, like setting tighter stop-loss orders, to minimize identified risks. Given the ever-evolving nature of global events and financial markets, stress testing should be periodically revisited to keep the trading system updated and ready for unforeseen challenges.
4.6. Alerts
An essential component during the testing process and after a strategy goes live is the alert system. This is a programmed feature that notifies traders when certain rules and risk management criteria are met. The purpose of the alert system is to ensure that traders don’t miss potential trading opportunities. Rather than continuously monitoring market data manually, traders can rely on the alert system to notify them when it’s time to make a move.
Alerts can take many forms, including pop-up notifications in a trading strategy system, emails, text messages through SMS and social media like Telegram, or even automated phone calls. They can be designed to signal both when to enter a trade (a buy or sell alert) and when to exit a position (such as a stop loss or take profit alert).
The alert system is typically customizable, allowing traders to fine-tune the conditions that trigger alerts based on their specific trend-following trading strategy and risk tolerance. They can also often be integrated into other software or platforms, enabling automated trading where trades are executed automatically when an alert is triggered. In addition, the alert system can play a critical role in risk management, sending warnings when certain risk thresholds are breached, helping traders avoid potentially costly mistakes.
An alert might be created in the form of a predefined syntax. A syntax ensures alerts are created and interpreted consistently. For instance, the alert syntax “a=applusd, q=10, o=market” instructs to buy 10 shares of Apple with USD at the current market rate. With a predefined format, alerts can be automated and integrated with other software more efficiently. Syntax support also makes it easier to track, record, and analyze alerts for performance review and strategy improvement.
Every trend-following trading system should contain an alert system and support a syntax to produce buy and sell alerts for several reasons:
A. Time-sensitive Decision Making
Markets move quickly, and sometimes, even seconds can make a significant difference to the profitability of a trade. An alert system informs traders immediately when a trading opportunity arises, allowing them to act promptly.
B. Error Mitigation
Alert systems can help prevent costly trading mistakes. If certain risk parameters are breached, the system can generate an alert to notify the trader, thus helping to prevent potentially risky trades.
C. Efficiency
It’s not feasible for traders to monitor all markets and assets continuously. Alert systems do this job, saving traders time and effort, and enabling them to focus on other strategic tasks.
D. Discipline
Buy and sell alerts based on a system’s rules promote discipline by signaling when it’s time to act according to the strategy. This prevents the trader from making impulsive decisions based on emotion.
E. Consistency
Consistently applying a strategy is key to long-term trading success. Alert systems help achieve this by automating the alert generation process based on the system’s rules, ensuring that the same criteria trigger each alert.
4.7. Integration Middleware Software
To execute trading alerts as orders on an exchange, a crucial role is played by middleware software. A middleware is a software that ensures smooth communication between different systems or applications by serving as a bridge for data exchange. Its significance in the operation of a trend-following trading system is paramount. Here’s an overview of how a middleware functions:
Step 1. Receive
The initial step in the middleware operation is to get data from a source. In this case, the middleware receives buying and selling info from the trading system through an alert.
For instance, the TradingView alert syntax “a=amzlusd, q=20, o=market” signals the bot to instruct the middleware to create a signal to buy 20 shares of Amazon with USD at the current market rate.
Step 2. Process
Once the data is received, the middleware layer’s role becomes vital in processing the data accurately following preset rules.
For a trading strategy, the middleware’s crucial task is converting the alert syntax into a format that meets the exchange’s specifications. It doesn’t just ensure accurate data processing but also needs to be robust and scalable, enabling it to perform this task quickly and effectively for the trading strategy
Step 3. Send
Ultimately, the middleware sends the processed information to a destination point. Drawing from our example, it will send a signal to buy 10 Apple shares at the current market rate to your account via the exchange’s API. To automate the conversion of the buy signal into an order, it’s essential to give the exchange advance permission to process such signals as orders for you.
ALPHA SHIFTER
5. TYPES OF TREND-FOLLOWING SYSTEMS
At Oxido Solutions, we distinguish three different types of trend-following systems: manual, automated, and hybrid. Here’s what each type means:
5.1. Manual
A manual trend-following system requires the trader to personally review market data, apply the system’s rules, and make trading decisions. While the system provides the rules, alerts, and tools for risk management, the trader is responsible for executing the trades. This approach allows for human judgement and intervention, but it can be time-consuming and requires discipline to stick to the rules.
5.2. Automated
In an automated trend-following system, the entire trading process is handled by a computer program. This includes analyzing data, applying the rules, generating the trading alerts and sending them to the middleware. The system uses predefined rules to make all trading decisions and can react to market changes almost instantaneously. Automated systems can operate 24/7 and remove the emotional element from trading, but they also require careful oversight to ensure the rules are working as intended.
5.3. Hybrid
A hybrid trend-following system combines elements of both manual and automated systems. It may use automation for some aspects of trading, such as data analysis and rule application, but leave the final decision to execute a trade to the trader. Alternatively, a hybrid system may support automated trade execution through a middleware, but allow for manual intervention when the trader feels it’s necessary. This approach balances the benefits of automation with the flexibility and oversight of manual trading.
6. ADVANTAGES OF TREND-FOLLOWING SYSTEMS
A well-implemented trend-following system can capitalize on market movements, both upwards and downwards, presenting potential profit opportunities. According to Oxido Solutions, here are the ten major advantages of such systems:
A. Systematic Approach
These systems provide a structured, methodical approach to trading that helps to remove emotions and biases from the decision-making process.
B. Clear Entry and Exit Points
Trend-following systems offer clear rules for when to enter and exit a trade, reducing the likelihood of premature or late trading decisions.
C. Profit Potential in Any Market Condition
Since trend-following strategies can profit from both rising and falling markets, they can be effective in any market condition.
D. Risk Management
Most trend-following systems have built-in risk management strategies that aim to protect the trader’s capital.
E. Adaptability
These systems can be applied to a variety of asset classes, including stocks, bonds, commodities, and currencies.
F. Automation Potential
The rule-based nature of trend-following strategies makes them well-suited to automation, which can increase efficiency and reduce the chance of human error.
G. Stress Reduction
Automated trend-following systems can reduce the stress associated with manual trading, especially in highly volatile markets.
H. Potential for Large Gains
These systems aim to capture significant market trends, which can lead to large gains when successful, often offsetting many smaller losing trades.
I. Accessible
Compared to some other trading strategies, trend-following can be relatively straightforward to understand, making it more accessible and suitable for both novice and experienced traders.
J. Performance Measurement and Improvement
With clear rules and data-driven decision-making, it’s easier to measure the performance of a trend-following strategy and identify areas for improvement.
7. DISADVANTAGES OF TREND-FOLLOWING SYSTEMS
Despite the many advantages, trend-following trading systems also come with several potential disadvantages. Here are is our downside list:
A. Lagging Indicators
Trend-following systems rely on historical data and can therefore lag behind the current market action. As a result, they might not always catch trends early and can be late in identifying reversals.
B. Whipsaw Losses
During periods of market volatility without a clear trend, trend-following strategies can lead to whipsaw losses — when a trader enters a position, but the direction changes quickly causing a loss.
C. Over-Optimization
There’s a risk of over-optimization in backtesting. Traders might tailor their system too closely to past data, which may not prove effective with future trends.
D. Long Drawdown Periods
Trend-following systems can have long periods of drawdowns (declines in the value of an investment or portfolio) when the market is range-bound or when trends reverse abruptly.
E. Complexity
Although the concept behind trend-following is relatively simple, in practice, determining the trend and when it changes can be complex. This also applies to incorporating risk management. Traders need to select and configure the right existing indicators or develop custom indicators for their system.
F. Need for Discipline
Traders need to stick to their system even during drawdown periods. This requires discipline, patience, and confidence in the strategy, which not all traders may have.
G. False Signals
All trend-following systems, especially in volatile markets, may give false signals which can lead to losses.
H. Reliance on Technology
Automated trend-following systems are heavily reliant on technology. Technical glitches, software failures, or connectivity issues can lead to significant issues.
I. Regular Monitoring and testing
Although these systems can be automated, they still require regular monitoring and testing to ensure they are functioning correctly and the rules are still applicable in the current market conditions.
J. Lack of Predictability
Trend-following systems are not designed to predict market tops and bottoms but rather to identify and follow existing trends. Traders looking to buy at the lowest point and sell at the highest point might find these systems unsuitable.
8. OXIDO SOLUTIONS’ TREND-FOLLOWING BOT SYSTEM
By now, you should have an idea of the theory behind trend-following systems and understand their strengths and weaknesses. But how do these systems operate in a real-world setting? In this chapter, we’ll outline how Oxido Solutions has set up its fully automated trend-following system. We’ll discuss our trading strategy, the rules we stick to and the technologies we employ. We share how we use Artificial Intelligence and how we solve the biggest challenge of trend-following systems.
8.1. Algorithmic Trading Strategy
Oxido Solutions employs a trend-following trading system to run a fully automated trend-following strategy for its Binance trading bot, Bybit bot, OKX trading bot and other crypto bots. This strategy aims to capitalize on volatility in the crypto derivatives market. The strategy identifies buying and selling opportunities for the Bitcoin Perpetual Futures trading pair on top-tier crypto exchanges. This identification primarily relies on technical analysis and is further validated by sentiment analysis.
Our algorithmic trading strategy consists of two trend-following algorithms: the Range Maker and ATR. Both are breakout strategies. This means that when the Bitcoin price breaks out of the range generated uniquely by both strategies, the trend-following system created a buy or sell alert under certain conditions. While the ATR generates trading opportunities for the 10-minute timeframe, the Range Maker does so for the 15-minute timeframe. This arrangement ensures that both algorithms hedge against each other to some extent. In other words, if one trading algorithm performs poorly in a given month, it often happens that the other algorithm performs better.
RESULTS
8.2. Trading Bot Rules
Oxido Solutions operates using a rule-based system. This systematic approach ensures that trading decisions are not influenced by human emotions and are consistent with pre-defined criteria. The rules are developed based on careful analysis to create a profitable and effective trend-following system. These are the key rules:
8.2.1. Position Types
Oxido Solutions’ algo system primarily considers three types of positions: long (l), short (s), and neutral (no position). The system enters a long position when it predicts an upward trend and a short position when a downward trend is anticipated. When the system does not identify a clear market trend, it may choose to hold a neutral position to mitigate potential risks.
8.2.2. Partial Close Position
Oxido Solutions incorporates the strategy of partially closing positions to derisk or take profits. This approach allows is to lock in some profits or reduce potential losses while still maintaining a position in the market to take advantage of any continuing trends.
8.2.3. Order Types
Both limit and market orders are used in the system. Limit orders allow our crypto trading bot strategy to specify a price at which it is willing to buy or sell an asset, providing control over execution price. Market orders ensure quick execution, useful in fast-moving markets.
8.2.4. No Dollar-Cost Averaging (DCA) or Pyramiding
Oxido Solutions does not employ DCA or pyramiding strategies. DCA involves investing fixed amounts at regular intervals, regardless of price, while pyramiding involves adding to positions as the price moves in a profitable direction. By excluding these strategies, Oxido prioritizes market trends and rule-based decision making.
8.3. Risk management
Here’s how Oxido Solutions applies risk management in its trend-following system:
8.3.1. Dynamic Stop Loss
A dynamic stop loss adjusts to market conditions and changes in the price of an asset. It moves with the market price, always maintaining a certain distance, unlike a traditional stop loss which is fixed. This allows Oxido Solutions to protect profits when prices are moving favorably, and limit losses when prices move against their position.
8.3.2. Leverage as a Risk Management Mechanism
Oxido Solutions uses leverage as a risk management tool. Leverage allows the firm to trade larger positions with a smaller amount of capital. While this can amplify profits, it can also magnify losses. Therefore, it is carefully managed to balance potential returns with the risks.
8.3.3. Isolated margin
Oxido Solutions employs isolated margin in its futures trading. This means the margin, or the amount of capital required to open and maintain a position, is isolated per position. This limits the risk to the amount of margin allocated to a specific futures contract, preventing losses from exceeding the initial margin and protecting other positions from being liquidated.
8.4. Technologies
The trend-following trading system of Oxido Solutions consists of a combination of custom applications, custom indicators, and third-party software. This approach has allowed us to avoid reinventing the wheel for certain aspects of our trading strategy. As a result, we were able to channel our efforts into developing customized functionalities to compete effectively with other trading bots, leveraging a robust foundational framework. Here’s how we’ve structured our trading infrastructure:
8.4.1. TradingView
TradingView is a web-based platform for traders and investors. It not only provides advanced charting tools, all the necessary trading data and social networking capabilities, but also the ability to create and share trading bot strategies. Oxido Solutions has developed its two trend-following strategies in TradingView’s coding editor using Pinescript, the platform’s programming language. The strategy was then extensively tested with the built-in testing tool before being launched live. TradingView’s alert system is used to generate buy and sell signals
8.4.2. Auto-optimizer
TradingView does not allow traders to automatically modify trading strategy parameters based on market fluctuations. This limitation can cause delays in strategy responses, potentially missing profitable trades or prolonging losses. Moreover, the constant need for manual tuning not only increases the risk of errors but also makes the process time-intensive. To overcome these limitations, Oxido Solutions developed an auto-optimizer application using Python and machine learning. This application identifies the optimal parameters for our trading bot strategy based on current market situations, ensuring consistent performance during market fluctuations.
8.4.3. Our Validation system
Oxido Solutions has incorporated an AI-based validation system into its trading system. The validation system uses the Transformers library. This open-source Python tool is designed for Natural Language Processing (NLP) tasks such as text classification, extraction, and generation. We use the Transformers library to supplement and validate our rule-based system, which produces buying and selling signals based on technical analysis. For instance, when a buying signal for Bitcoin is generated but our AI validation identifies a negative sentiment for Bitcoin, this insight might affect the final trading decision. Our AI validation system relies on two key components: sentiment analysis and event extraction. Sentiment analysis reviews Bitcoin-related news, social media posts, and other text data to derive a sentiment score, which then influences our trading bot strategy. Conversely, event extraction captures important details from Bitcoin events, further guiding our trading decisions.
AI TRADING
8.4.4. Alpha Shifter
Back in 2018, Oxido Solutions introduced the initial version of the Range Maker. While the backtest outcomes looked promising, the live results of this crypto bot algo didn’t meet expectations. The challenge was linked to the third-party middleware solutions we used to facilitate data exchange between TradingView and brokers; unfortunately, these solutions didn’t perform as desired. They exhibited unreliable order execution and lacked essential management and security features. This forced us to create Oxido Solution’s proprietary in-house enterprise middleware solution, which we’ve been using since 2019. Furthermore, recognizing the demand among fellow professional traders for top-tier trading middleware, we decided to make our middleware, known as Alpha Shifter, available to the public. Starting from 2023, hedge funds, family offices, and other professional entities can access Alpha Shifter through a software licensing agreement.
8.4.5. MetaTrader5
While TradingView caters to most elements of our trend-following trading system, we do not want to be entirely dependent on it. Even though we have never experienced downtime or other negative issues, it doesn’t guarantee that this will be the case in the future. Therefore, as a fallback, we use another popular trading platform: MetaTrader 5. Within this platform, we have developed the same strategy in the MT5 programming language as we have on TradingView. The trading bot alert system on both platforms is also set up similarly. If a trading alert from TradingView isn’t forwarded to Alpha Shifter, the intent is that the same trading signal from MT5 will automatically be sent to Alpha Shifter.
8.5. Our sideways filter
One of the biggest challenges with trend-following trading bot strategies is their suboptimal performance during weak trends. Oxido Solution has introduced a solution with its sideways filter. This technical indicator recognizes sideways price movements early on. As a result, our trading system avoids trades during weak trends and only acts when there’s a strong trend, reducing losses and increasing profits.
9. THE FUTURE OF TREND-FOLLOWING SYSTEMS
Oxido Solution believes that to be successful in large-scale trend-following algorithmic trading, one must possess expertise across various disciplines. That’s why our team is not only equipped with seasoned traders and IT professionals but also crypto lawyers and other legal experts. This allows us to offer a diverse range of products in the field of trend-following trading and stay updated with the latest developments in our domain. From our 360-degree perspective, we also have an insight into where the future of trend-following systems might be headed. Here’s our vision on that from various angles:
9.1. Our IT perspective
From an IT perspective, we see a significant role for quantum computing and artificial intelligence. The fusion of these technologies could redefine the boundaries of what’s possible in the realm of trend-following trading systems.
9.1.1. Quantum computing
Quantum computing is a type of computing that utilizes principles of quantum mechanics, a fundamental theory in physics that describes the behaviors of matter and energy on the smallest scales (atomic and subatomic). Unlike classical computers that use bits (0s or 1s) to process information, quantum computers use quantum bits or qubits, which can exist in a state of 0, 1, or both simultaneously (a phenomenon called superposition). This ability to be in multiple states at once, along with other quantum phenomena like entanglement, allows quantum computers to perform certain complex calculations much faster than traditional computers.
Quantum computing is still at its infancy, but as this tech grows, it can change how trading systems work. Here’s how quantum computing might be valuable for trend-following trading systems in the future:
A. Speed and Computational Efficiency
Quantum computers can potentially process vast amounts of data faster than classical computers. Trend-following trading systems rely on analyzing large sets of historical and real-time market data to identify and exploit price trends. The computational advantage of quantum computers could lead to quicker identification of emerging trends, allowing traders to act faster than competitors.
B. Simulating Complex Financial Models
Financial markets are inherently complex, and their behavior can sometimes resemble the behaviors seen in quantum systems. Quantum computers are naturally suited to simulate quantum systems and, by extension, can be used to model intricate market behaviors more accurately than classical computers. This could provide trend-following systems with more accurate predictive models.
C. Optimization
Trend-following trading often involves optimizing various parameters, such as entry and exit points, portfolio allocation, risk management, etc. Quantum computing has shown promise in solving complex optimization problems more efficiently than classical computing. Quantum algorithms, like the Quantum Approximate Optimization Algorithm (QAOA), could be used to find optimal trading strategies in trend-following systems.
D. Quantum Machine Learning
Quantum machine learning (an intersection of quantum computing and machine learning) can potentially lead to faster training of models and better pattern recognition. In a trend-following context, this could lead to trading algorithms that are more adaptive and can identify subtle market patterns that might be too complex for classical algorithms.
E. Cryptography and Security
Quantum computers have the potential to break many current cryptographic techniques. In response, new quantum-safe cryptographic methods are being developed. Trend-following systems, like all financial systems, need robust security to protect against malicious attacks. Adopting quantum-safe security measures will be essential in a world where quantum computers are widespread.
F. Risk Management and Scenario Analysis
By leveraging the power of quantum computing, traders can simulate various market scenarios at speeds and complexities not possible today. This can lead to better risk assessments and more informed trading decisions.
9.1.2. Artificial intelligence trading
The advancements in the field of AI continue to offer a range of improvements and optimizations for trading systems. New applications of AI methods like machine learning, NLP and generative AI will improve the efficiency, reliability, security, and user experience of trading systems. It can significantly reduce the costs of operation, maintenance, and even development of these systems. Here is a detailed outcome of the benefits AI can bring:
A. Efficient Data Processing and Management
Advanced AI algorithms can handle massive datasets swiftly and efficiently. This means that trading systems can process and manage historical data, real-time data feeds, and more, with greater speed and accuracy.
B. Enhanced Security
AI can be employed to monitor, detect, and prevent security threats. Machine learning models can recognize anomalous behavior that might signify a security breach, providing real-time threat intelligence.
C. Scalability
AI-driven cloud infrastructures can auto-scale based on the demand. For instance, during high-frequency trading periods when there’s a surge in data, AI-driven cloud systems can automatically allocate more resources.
D. Infrastructure Monitoring and Optimization
AI can predict hardware failures or bottlenecks in the system before they become critical. It helps in proactive maintenance, ensuring maximum uptime.
E. Resource Optimization
AI can optimize queries, improve data retrieval times, and ensure that computational resources are utilized efficiently. This helps in reducing costs and ensuring smoother operations.
F. Automated Workflow
Advanced AI can automate many IT-related tasks such as data backup, server maintenance, and even some aspects of software updates, reducing manual intervention and related errors.
G. Customized User Interfaces
AI-driven user interfaces in trading systems can be adaptive and personalized based on the user’s behavior and preferences, improving user experience.
H. Natural Language Processing (NLP) for Support
Chatbots and virtual assistants, driven by NLP, can offer immediate IT support, answer frequently asked questions, and guide users through system features.
I. Network Optimization
AI can help in optimizing the network paths, reducing latencies which are crucial for trading systems, especially high-frequency trading.
J. Development and Testing With AI
Trading bot software can be tested using automated test cases which evolve with time, ensuring that any new changes do not introduce errors.
K. Continuous Integration and Deployment
AI can optimize the CI/CD pipeline, which is a series of automated processes that allow algo developers to reliably and efficiently test and deliver code changes more frequently and with greater accuracy. This ensures that new features, patches, or updates are integrated seamlessly without disrupting the trading environment.
9.2. Our trading take
The future of trend-following trading systems, from a trading perspective, will be influenced by various factors ranging from technological advancements to changing market dynamics. Here’s a speculative overview of what the future might hold:
A. Improved Data Sources:
i. Alternative Data
The use of non-traditional data sources (like satellite images, social media sentiment, etc.) will become more mainstream, offering unique insights into potential market-moving trends.
ii. High-Frequency Data
With the rise in HFT bots, trend-following systems might need to be more adaptive to shorter, micro-trends in addition to long-term trends.
B. Adaptive Risk Management
As markets become more interconnected and influenced by global events, trend-following systems will likely incorporate more sophisticated risk management tools and techniques to counteract increased volatility and black swan events.
C. Market Dynamics:
i. Crowding
As more traders adopt trend-following strategies, there might be instances of ‘crowding’ in certain trades. This can lead to diminished returns and increased potential for reversals.
ii. Evolution of Markets
The emergence of new asset classes (e.g., cryptocurrencies) and trading venues will provide trend-following systems with more opportunities but also new challenges.
D. Emphasis on Diversification
To counteract potential market changes and ensure resilience, there will likely be an emphasis on diversifying trading across multiple assets, timeframes, and even trading styles.
E. Human-Machine Collaboration
While automation will play a significant role, the human touch will remain crucial. Traders will work alongside advanced systems to provide qualitative insights, intuition, and oversight.
F. Globalization and Interconnectedness
Trend-following systems will need to account for global macroeconomic factors, geopolitical events, and inter-market correlations more than ever.
9.3. Our legal view
As AI becomes increasingly integrated into financial services, the legal landscape surrounding trend-following systems and AI in trading will likely undergo significant changes. Drawing from the latest Artificial intelligence regulation initiatives, our AI lawyers and other legal experts can predict the following:
A. Risk-Based Classification
Given that the majority of regulators are classifying AI systems based on risk, it is probable that trend-following systems will also be evaluated based on their potential impact on financial markets and individual investors:
i. High-Risk Systems
Trend-following systems with significant control over large capital or those which can influence market movements may be classified as high-risk and subjected to more rigorous oversight and regulatory requirements.
ii. Limited or Minimal Risk
Smaller, less influential trend-following algorithms may face less stringent regulations.
B. Transparency and Accountability
C. Decision Explanation
There might be a requirement for AI system providers to explain a decision made by an AI system upon request. In the context of trend-following systems, if an investment decision leads to significant losses or irregularities, stakeholders might have the right to demand an explanation.
D. Human Oversight
There may be mandated human intervention in decisions made solely by AI systems. This means that trend-following systems might be required to operate under human supervision, especially when making high-value or high-risk trades.
E. Data Rights and Privacy
Trend-following systems rely heavily on data. Many initiatives emphasize rights to privacy and protection of personal data. As a result, AI trading platforms might face stricter regulations concerning the data they can use, how they store it, and for how long.
F. Governance, Liability, and Sanctions
There will likely be requirements for AI system providers to establish structures ensuring the safety of AI systems. Trend-following systems might be subjected to regular assessments and audits to ensure they operate as intended without causing undue market disruption.
In terms of liability, there might be an emphasis on both the AI’s developers and users. If a trend-following system were to malfunction and cause significant market disruption, the developers, as well as the trading entity deploying the AI, might be held liable.
Non-compliance with the set regulations might attract hefty penalties. In Brazil, for instance, non-compliance can attract fines of up to 50 million Brazilian reals or 2% of a company’s turnover.
G. International Collaboration and Alignment
The EU and U.S., among other global players, will play a pivotal role in shaping the future of global AI governance, including AI in trading. While there may be differences in approaches, collaborative efforts like the EU-U.S. Trade and Technology Council hint at a future where there’s a convergence of standards and practices, fostering smoother international trade and AI technology exchanges.
H. Ethical Considerations
Besides the legal implications, there will be a broader discussion on the ethical use of AI in trading. Prohibited systems, like those that exploit vulnerabilities, might be extended to AI trading systems that exploit market vulnerabilities unethically.
All in all, we believe that the legal future of trend-following trading bot systems will likely be characterized by more stringent oversight, transparency, accountability, and international collaboration. As AI becomes a staple in trading, regulators worldwide will be hard-pressed to ensure that its use remains fair, transparent, and doesn’t compromise the stability of global financial markets.
CRYPTO LAWYER
10. FINAL THOUGHTS
Trend-following trading bot systems can be a powerful financial tool, offering an unparalleled balance of risk and reward when used effectively. These systems allow traders to leverage the consistent directional trends of asset prices, capturing profits in both ascending and descending markets. This is made possible due to the cyclical nature of financial markets, which is largely driven by collective human emotions.
The optimism and excitement of the masses drive bull markets, creating strong upward trends as everyone rushes to buy and participate in the expected price ascents. On the flip side, widespread fear and despair stir bear markets, leading to downward trends as everyone hurries to sell off their assets and avoid potential losses. This emotional tug of war, between fear and greed, gives birth to market trends. These intense, emotion-fueled trends emphasize why trend-following systems remain both relevant and effective.
The key to unlocking the potential of trend-following systems lies in carefully designing strategies, rigorously testing them, applying disciplined execution, and using middleware software like Alpha Shifter smartly. Despite their advantages, it’s important to be aware of the hurdles that can arise with these systems, such as latency issues, the need for high-quality data, dealing with sideways market periods, and avoiding the trap of overfitting strategies to past data.
As we march forward in the digital age, the evolution of technology, specifically the advances in artificial intelligence and quantum computing, promises to influence and enhance the capabilities of trend-following systems. The future looks bright, and trend-following systems are geared up for it.
