Algorithmic Trading involves the use of computer algorithms to automatically execute purchase or sale operations of financial assets on the markets (including cryptocurrencies). These algorithms are programmed to make decisions based on real-time market data and specific parameters, without human intervention. The main advantages of algorithmic trading include the ability to trade based on objective criteria and the ability to manage multiple assets and strategies simultaneously. As well as very fast purchasing and selling times.
WHAT TO EVALUATE
-Algorithm development: These algorithms can vary in complexity, from simple order execution based on predefined rules to the use of advanced machine learning models
-Market data collection: Data is collected from exchanges, news feeds, technical indicators, historical data and more. This data is used to analyze market conditions and make trading decisions
-Data analysis: algorithms analyze market data to identify patterns, trends, arbitrage opportunities (buy on x at a lower price and instantly sell on y at a higher price) or trading signals. This analysis may include the use of technical indicators, fundamental analysis and complex mathematical models
-Order Execution: Once the algorithm is set to execute a trade, it automatically generates a buy or sell order. This process happens in milliseconds to take advantage of market opportunities before they can change
-Risk monitoring and management: Algorithms constantly monitor their positions and market conditions in real time. They are also programmed with stop losses (for risk mitigation) and take profits (when certain objectives are reached)
-Optimization: Algorithms are constantly updated based on evolving market conditions and previous trade results
MAIN TECHNICAL INDICATORS
The technical indicators are the same as those used manually, except that the bot interprets the data independently. The main ones are:
Moving Average: this indicator calculates the average of the prices of a financial asset over a specific period of time. Moving averages can be simple (SMA) or exponential (EMA), and are used to identify market trends. The simple moving average tends to respond more slowly to recent changes in prices, as it takes into account all the data over the specified period (it is simply the sum of an asset's closing prices divided by the period considered). The EMA, on the other hand, assigns greater weight to more recent data and decreasing weight to older data. This makes the EMA more sensitive to recent changes in prices (the formula is more complex but it turns out that the final result is influenced by the most recent close of an asset's prices)
MACD (Moving Average Convergence Divergence): The MACD is a momentum indicator that compares two exponential moving averages. The intersections between the MACD and its signal line can indicate entry or exit points. MACD Line is the difference between the 12-period exponential moving average and the 26-period exponential moving average. The MACD line reflects the short-term versus long-term price change. The signal line is a 9-period exponential moving average of the MACD line. It is used to generate buy or sell signals when it crosses with the MACD line.
The histogram represents the difference between the MACD line and the signal line and provides further information on the momentum of the trend. When the histogram is above zero, it indicates bullish momentum; when it is below zero, it indicates bearish momentum.
The MACD Crossover must be considered (when the MACD line crosses the signal line from top to bottom, it can indicate a sell signal. On the contrary, when it crosses from top to bottom, it can indicate a buy signal) and the divergence (divergences between the MACD histogram and the underlying price can suggest a possible trend reversal)
RSI (Relative Strength Index): RSI measures the relative strength of an asset by comparing gains and losses over a short time (usually 14 days). The RSI is expressed as a value between 0 and 100 and is based on calculations involving recent gains and losses.
For each day of the specified period, calculate the difference between today's closing price and yesterday's (used to evaluate profit and loss). If the price has risen, it is a profit; if it went down, it's a loss. It also averages your gains and losses: add up all your gains and losses over the 14 days and calculate the separate average for your gains and losses. An RSI above 70 can indicate that an asset is overbought, while an RSI below 30 can indicate that it is oversold
: This indicator includes a moving average with upper and lower bands that expand or contract based on market volatility. Bollinger bands are used to measure volatility and locate reversal points. They are made up of 3 bands: upper (this represents two standard deviations above a 20-period simple moving average of closing prices. The upper band tends to resist upward price movements), lower (this represents two standard deviations below the 20-period simple moving average (the lower band tends to resist downward price movements) and the central moving average (this is positioned in the middle between the two bands. The central moving average is used to evaluate the trend). Bollinger Bands provide insight into market volatility and suggest potential reversal points when prices extend outside the bands
Stochastic Oscillator: the stochastic is an indicator that measures the speed and amplitude of price movements. It can help identify overbought or oversold conditions. This oscillator measures the position of the price relative to the high and low range of a specified period. It produces values between 0 and 100. When the stochastic exceeds the value 80, the asset is considered overbought, which can suggest a possible downward correction. When the stochastic falls below the value of 20, the asset is considered oversold, which can indicate a possible upward correction. Furthermore, crossings of the two stochastic lines can generate buy or sell signals
MATHEMATICAL MODELS
GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model: this model is used to analyze price volatility and predict future volatility based on past performance
Black-Scholes Model: this model is used to calculate the price of options and is based on stochastic differential equations
Linear Regression Model: it tries to identify the relationship between a dependent variable (for example, the price of an asset) and one or more independent variables (for example technical indicators) through a regression line
Mean Reversion Model: this model is based on the idea that prices tend to revert towards a mean over time. Algorithms based on this model try to exploit price movements that deviate from the average
Machine Learning Model: These models use machine learning algorithms to analyze historical data and identify patterns or trends in the markets. This may include regression algorithms, decision trees, neural networks, etc
HOW A BOT WORKS
Algorithmic trading bots aim to exploit trading opportunities in a specific market, such as the cryptocurrency market. The bot implements a "mean reversion" strategy, which means it tries to take advantage of price movements that deviate from the historical average.
Generally we have:
-Data collection: The bot collects real-time market data, such as cryptocurrency prices, buy and sell orders, and other market indicators
-Moving average calculation: the bot calculates the simple moving average (SMA) of cryptocurrency prices over a given period of time
-Identification of possible profit opportunities: The bot constantly monitors the current price of the cryptocurrency compared to its SMA in a certain time interval. If the price deviates significantly above or below the average, the bot considers this a possible opportunity
-Generating an order: Let's assume that the current price is significantly below the previously calculated 20-day SMA, indicating a possible buying opportunity according to the "mean reversion" strategy. The bot then generates a purchase order with a certain quantity
-Order Execution: The bot automatically sends the buy order to the market via the trading platform. The order is executed if there are sellers willing to sell at the price specified by the bot
-Position tracking: Once the order is executed, the bot constantly monitors the position. May have a set profit target or stop-loss level to limit losses in case of an error and the market moving in the opposite direction
-Closing the position: If the price returns towards the 20-day SMA or reaches the set profit target, the bot automatically closes the position, selling the asset. Otherwise, if the price continues to fall and reaches the stop-loss level, the bot closes the position to limit losses
-Repetition of the process: bot continues to execute this strategy on a continuous basis, constantly looking for trading opportunities according to the "mean reversion" strategy
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