Basics of algorithmic trading signals and the technology behind it: AI, technical indicators and more. Profit from the best trading strategies and signals.
One way to make your trading decisions completely unbound by fear and greed is by utilizing algorithmic trading signals. This relatively new technology is changing the crypto landscape with incredible efficiency and precision.
So, it’s not a surprise that many traders taking advantage of its many possibilities are unlocking immense benefits for their portfolios. And once you’re done reading this article, you can as well.
That said, join us as we explore the algorithmic trading signals and the technology behind it.
What Are Trading Algorithms in Crypto?
A crypto signal trading algorithm is a mathematical program that tells you when to buy or sell cryptocurrencies, futures, or derivatives at a given price point and time in order to make a profit.
They provide signals based on market analysis, technical analysis, and other preprogrammed factors. Trading signals supplied by a crypto trading algorithm are called algorithmic or algo crypto trading signals.
Furthermore, they are similar to algo trading signals in the typical financial market. Algo trading signals have the apparent advantage of not being limited by fear and greed, which are human emotions that even the most seasoned trading experts possess.
In addition, they are often very precise and can save you lots of time. However, they have a couple of setbacks, which we’ll discuss later in this article.
The Structure of Algorithmic Trading Signals
The average algorithmic trading signal comprises the following:
- An asset name/trading pair that tells you the cryptocurrency or crypto pair to trade, e.g., ETH or BTC/USDT,
- A trade action that could either be buy or sell,
- The entry price to buy the cryptocurrency,
- The price point to take profit,
- And a stop-loss target, which is the cryptocurrency’s minimum projected value at which you must withdraw your funds.
How Do Trading Algorithms Generate Signals?
Professionals train trading signal algorithms using advanced machine learning technology. This helps them properly follow prompts and read through enormous amounts of market data to provide accurate signals.
In addition, these algorithms are programmed with trading strategies experienced traders use to make price predictions.
These include:
- fundamental analysis,
- technical strategies,
- on-chain analysis, and
- reinforcement learning.
Let’s talk about them below.
Fundamental Analysis
Fundamental analysis deeply examines a cryptocurrency or blockchain token and determines whether it is overvalued or underpriced, which helps make long-term forecasts.
It involves checking a crypto or blockchain project’s real-world application, user engagement, and other factors to determine whether it will increase in price in the future or drop due to empty hype (for instance, factors that drive FOMO on crypto twitter)
Other indicators considered in fundamental analysis include transaction volume, total value locked, whitepaper, and market cap, among many others.
Technical Analysis
Technical Analysis involves predicting where a crypto’s price will be based on its previous price movement patterns. Unlike fundamental analysis, technical analysis barely cares about whether or not a crypto asset has real-world applications.
It uses historical data like trading volume and price movements.
Several strategies are involved when using technical analysis to make signals.
These include the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Bollinger Bands, which we’ll discuss below.
On-chain Analysis
On-chain analysis differs slightly from fundamental and technical analysis, although it shares similarities. When making this kind of analysis, you typically consider the reason behind mass buying and selling on a blockchain to know how its price will move.
On-chain analysis involves indicators like the number of active addresses, realized market cap, liquidity flow, price catalysts, and others. A proper understanding of how to use on-chain tools could be the key to creating the best strategy to grow your portfolio:
On-chain Tools to Add to Your Arsenal:
Algorithmic Trading Signals with Reinforcement Learning (RL)
Experts also use reinforcement learning to train algorithms to provide accurate signals.
Reinforcement learning is a type of machine learning that uses a reward/penalty system to condition an algorithm, the agent, into making the right decisions by interacting with an environment.
Confusing right? Let’s take a step back.
Two players are involved in reinforcement learning: the environment and the agent.
Let’s explain these terms.
- Agent: Think of the algorithm as the agent. Its job is to provide trading signals.
- Environment: The crypto market is the environment. It’s where the agent operates.
- Actions: The agent takes actions, which, in this case, is giving buy or sell signals.
- Rewards/Penalties: The agent receives rewards or penalties based on the performance of its signals. For example, it gets a reward if a recommended trade results in profit and a penalty if it leads to a loss.
- Learning: Through trial and error, the algorithm or AI learns to generate more accurate signals that maximize rewards and minimize penalties over time.
Technical Algorithmic Trading Strategies
As aforementioned, technical analysis deals with a cryptocurrency’s numerical data. This makes it one of the most critical metrics to program a trading signal algorithm.
Let’s take a deeper look at these technical algorithmic trading strategies.
RSI Trading Strategy and Signals
RSI refers to the Relative Strength Index. It is an indicator that tells you whether a cryptocurrency, derivative, or future is overbought or oversold. Indicators that function this way are called momentum oscillators.
The RSI measures price movement on a scale of zero to one hundred. In typical conditions, an RSI below 20 means the market is oversold, and the price will likely increase, making it the best time to buy.
Conversely, if the RSI is above 70, the market is overbought, and the price will most likely drop soon, indicating that a sell decision is appropriate.
These are popularly used RSIs, but oversold and overbought levels can change depending on whether the market is bullish (on an upward trend) or bearish (on a downward trend).
MACD Trading Strategy and Signals
Like the RSI, the Moving Average Convergence Divergence (MACD) indicator is a momentum oscillator.
The Moving Average Convergence Divergence (MACD) calculates the difference between an asset’s 12-day and 26-day Moving Averages to determine the best buying and selling positions.
Here are some important terms you need to note about MACD.
- MACD Line (Blue): This line represents the difference between a short-term (12-day) and a long-term (26-day) moving average.
- Moving Averages: A moving average is a statistical calculation used to analyze the average price of a cryptocurrency over a specified period, in this case, 12 days and 26 days.
- Signal Line (Orange): The signal line is the 9-day moving average of the MACD line.
- Crossovers: When the MACD line crosses above the signal line, this suggests a bullish trend is occurring, and you should buy. However, when the MACD line crosses below the signal line, it indicates a likely bearish trend, meaning you should sell.
For an in-depth analysis of the MACD technical indicator check out our blog post by crypto trade Xilam.
Bollinger Bands Trading Strategy and Signals
Bollinger Bands are named after John Bollinger, its inventor, and like those we’ve mentioned above, they’re commonly used in typical financial markets.
Here’s how it works:
- Upper Band and Lower Band: Bollinger Bands consist of three lines – a middle line that is usually a simple moving average (SMA), and an upper and lower band that are above and below the SMA.
- Volatility Indicator: The distance between the upper and lower bands is determined by volatility. The bands widen in times of high volatility, and in periods of low volatility, they contract.
- Overbought and Oversold Conditions: When the price of the crypto asset touches or exceeds the upper band, it might be considered overbought, signaling a buy opportunity. Conversely, when the price touches or falls below the lower band, it could be considered oversold, indicating an exit point.
- Reversal Signals: Bollinger Bands can also identify potential reversal points in the market. If the price is near the upper band and starts moving back toward the middle line, it might suggest a reversal to the downside. Similarly, if the price is near the lower band and starts moving back toward the SMA, it might indicate a reversion to the upside.
Advantages of Using Trading Signal Algorithms
Speed and Efficiency
Using crypto signal algorithms can save you tons of time when making buying and selling decisions, as they can provide these signals faster than any human trader can.
Zero Emotional Bias
Fear and greed are the primary drivers of human inefficiency in crypto trading. Trading signal algorithms only follow predefined rules, making all their signals void of the impulsiveness these emotions cause.
Consistency
Using algorithmic trading signals ensures consistent execution of trading strategies. Algorithms follow preprogrammed trading instructions precisely, reducing the impact of human errors or inconsistencies.
Backtesting and Optimization
Algorithms allow for thorough backtesting; that is, they enable you to evaluate a trading strategy using historical market data for validation. This can lead to better-informed decisions and the optimization of trading strategies.
Disadvantages of Using Trading Signal Algorithms
Over-reliance on Historical Data
Algorithms depend on historical data for backtesting. If market conditions change unexpectedly, the algorithm may not perform as expected, and you could lose liquidity.
Lack of Adaptability to Unforeseen Events
A single tweet from a crypto influencer can set incredible bullish or bearish trends in motion. Algorithms cannot account for that and other unforeseen events in the crypto market, so depending on algorithmic trading signals can put your portfolio at risk.
Complexity and Over-Optimization
Overly complex algorithms may also lead to over-optimization, where the algorithm performs exceptionally well on historical data but struggles in real-time conditions.
Conclusion
Like AI signals, Algorithmic trading signals solve the problem of human inefficiency and emotions, ensuring you only receive signals based on preprogrammed trading strategies.
But trading algorithms also come with risks such as overcomplexity and inadaptability. So, before you use one, it’s best to consider these setbacks.
Make sure only to trade crypto assets you’re comfortable with losing, as crypto market conditions can be unpredictable sometimes. Always consider setting relatively safe stop loss levels before engaging a set-up. While you may make a lot of profits using its product, Crypticorn is not liable for any financial losses you incur when trading.
FAQ
Is algorithmic trading good or bad?
The benefits of algorithmic trading outweigh its cons, making it lean more towards being good. However, while using signals generated by crypto trading algorithms can produce positive results and profits, it doesn’t account for market volatility and can sometimes have negative consequences.
Does algorithmic trading work?
Yes, algorithmic trading works. It uses several preprogrammed trading strategies to provide precise and consistent signals.
How to start algorithmic trading?
To start algorithmic trading, you need to select an algorithmic signal provider. Next, you prompt it based on the crypto or futures pair you want to trade and execute the signals it provides either manually or via an algorithmic trading bot.
