Quantum Algo was built on groundbreaking concepts within the field of technical analysis; focusing to a large degree on enhancing user experience and collecting feedback. At present, modern investors are blessed with a number of tools and often an excess of data that need be organized and easily accessed. However, being spoiled for choice with many applications available can leave many feeling confused and overwhelmed. We set out to change this!
In order to address this issue, Quantum Algo adopted a community-driven, adaptive approach, which resulted in the creation of a powerful all-in-one tool suite. This powerful suite seamlessly integrates with popular charting applications, providing investors with a clear and concise technical analysis experience, helping to foster the ultimate goal of trading smarter. Offering 14 day risk free trials, we encourage you to join Us Today!

Role of Algorithms in Capital Markets

Algorithms are widely used in the stock market to make decisions based on real-time market data. They aim to exploit market inefficiencies and minimize transaction costs. Algorithmic trading is a significant force in the stock market, responsible for a large percentage of daily trading volume.
Trading algorithms automate the process of executing trades and can help traders make informed decisions by analyzing market data and identifying trade opportunities. They are used in a variety of ways to execute trades in financial markets, providing benefits in terms of speed, efficiency, and accuracy. The effectiveness of trading algorithms depends on factors such as the quality of the algorithms, market conditions, and the trader’s ability to execute them effectively. Overall, trading algorithms play a critical role in helping traders make informed decisions and execute trades in a timely and efficient manner.
Risk algorithms are used in trading to measure and manage risk in financial markets. They can help traders identify potential risks and assess the impact of various scenarios on their trades and portfolios. Risk analytics algorithms can also help traders optimize their portfolios based on their risk tolerance and investment objectives. In addition, these algorithms can be used to implement risk management strategies, such as stop-loss orders, to help limit losses in volatile markets.
Portfolio optimization involves using mathematical models and algorithms to create an investment portfolio that maximizes returns while minimizing risk. It is based on the idea of diversification, which involves spreading investments across different asset classes to reduce risk. Portfolio optimization algorithms use historical data and market trends to identify the optimal mix of investments based on an investor’s risk tolerance and investment objectives. By creating a diversified portfolio with an appropriate level of risk, investors can achieve their financial goals while minimizing the potential for losses.
Pricing algorithms are used in financial markets to determine the fair value of financial instruments such as stocks, bonds, and derivatives. These algorithms use a variety of factors to estimate the price of an asset, such as market data, historical trends, and economic indicators. They are used by market makers, traders, and investors to determine the optimal price to buy or sell an asset. Pricing algorithms can help improve price transparency in financial markets and ensure that prices are fair and efficient. However, the effectiveness of pricing algorithms depends on the quality of the data and the accuracy of the models used to estimate prices.

Crunching the Numbers: How Algorithmic-Based Indicators Signal Trading Opportunities

Algorithmic-based charting indicators use advanced mathematics and predictive AI to generate trading signals by analyzing past price and volume data to identify patterns that can indicate potential future movements in the market. These indicators rely on complex statistical models and machine learning algorithms to identify trends, momentum, and other factors that can impact the market. In addition to these mathematical techniques, algorithmic-based charting indicators may also use machine learning algorithms to identify patterns and make predictions. Machine learning algorithms can analyze large amounts of data and learn from past market behavior to make predictions about future market movements. Overall, algorithmic-based charting indicators use advanced mathematics and predictive AI to generate trading signals by analyzing past price and volume data and identifying patterns and trends that can indicate potential future movements in the market.

Quantum Algo Indicators: Using Tomorrow's Technology For Today's Trading

Moving Averages
A moving average indicator is a technical analysis tool used to smooth out fluctuations in a stock’s price or other financial data over a specified time period. It is calculated by taking the average price of a security or other financial instrument over a specified period of time, such as the last 50 days or the last 200 days.

 

For example, a 50-day moving average is calculated by adding up the closing prices of a stock for the last 50 days and dividing the sum by 50. This value is then plotted on a chart and updated each day as new data becomes available.

 

Moving averages are often used to identify trends in a stock’s price or other financial data, and can be helpful in determining support and resistance levels. Shorter moving averages, such as a 20-day or 50-day moving average, are more sensitive to recent price changes and can help identify shorter-term trends, while longer moving averages, such as a 200-day moving average, are more indicative of long-term trends.
Relative Strength Index (RSI)
The RSI indicator, or Relative Strength Index, is used to measure the strength of a security’s price action by comparing its upward and downward price movements over a specific time period.

 

The RSI is calculated based on the average gains and losses of a security over a specified time period, typically 14 days. The formula for calculating the RSI is as follows:

 

RSI = 100 – (100 / (1 + RS))

 

(Where RS = Average gain of up periods / Average loss of down periods.)

 

The RSI value ranges from 0 to 100, with readings above 70 typically indicating overbought conditions, and readings below 30 indicating oversold conditions. Traders use the RSI to identify potential trend reversals or market turning points, as well as to confirm the strength of a current trend.
Bollinger Bands
Bollinger Bands are technical analysis tools used to determine the volatility and potential price range of a security over a given period of time.

 

The Bollinger Bands are plotted on a price chart and consist of three lines:

 

a centerline and two outer bands, which are derived from a simple moving average and standard deviation.

 

The centerline is typically a 20-day simple moving average, and the two outer bands are positioned two standard deviations away from the centerline, one above and one below. The standard deviation is a measure of the variability of a security’s price over a period of time, and by using two standard deviations, the Bollinger Bands encompass approximately 95% of the price action, creating a range within which the price is expected to move most of the time.

 

When the price of a security moves towards the upper or lower Bollinger Band, it may indicate that the security is overbought or oversold, respectively, and may be due for a reversal.
MACD (Moving Average Convergence Divergence)
The Moving Average Convergence Divergence (MACD) indicator is used by traders to identify trends and momentum in a security’s price movement. The MACD is based on the difference between two moving averages, typically the 12-day exponential moving average (EMA) and the 26-day EMA.

 

The MACD is calculated by subtracting the 26-day EMA from the 12-day EMA. This difference is plotted on a chart along with a 9-day EMA, which is referred to as the signal line. The MACD and the signal line can be used to identify bullish and bearish signals, as well as potential trend reversals.

 

When the MACD crosses above the signal line, it is considered a bullish signal, indicating that the security’s price is trending higher. Conversely, when the MACD crosses below the signal line, it is considered a bearish signal, indicating that the security’s price is trending lower.

 

In addition to the MACD and signal line, the indicator also includes a histogram that shows the difference between the MACD and the signal line, providing an additional visual representation of the trend and momentum of the security’s price.
Stochastic Oscillator
The Stochastic Oscillator is used by traders to measure the momentum of a security’s price and identify potential trend reversals. The oscillator is based on the idea that as prices increase, closing prices tend to be closer to the high end of the trading range, and as prices decrease, closing prices tend to be closer to the low end of the trading range.

 

The Stochastic Oscillator is calculated by comparing a security’s closing price to its trading range over a specified period of time, typically 14 periods. The formula for calculating the Stochastic Oscillator is as follows:

 

%K = (Current Close – Lowest Low) / (Highest High – Lowest Low) * 100

 

%D = 3-period moving average of %K

 

Where %K is the raw measure of momentum and %D is a 3-period moving average of %K.

 

The Stochastic Oscillator is displayed as two lines: %K and %D. The %K line is the more sensitive of the two and is prone to giving false signals, while the %D line is a smoothed version of the %K line, providing a more reliable signal.

 

The Stochastic Oscillator is typically plotted on a scale of 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions. Traders often use the Stochastic Oscillator to identify potential trend reversals or to confirm the strength of a current trend.
Fibonacci Retracement
Fibonacci retracement is used to identify potential levels of support and resistance in a security’s price movement. The tool is based on the idea that the price of a security will often retrace a predictable portion of a move, after which it may continue in the same direction.

 

Fibonacci retracements are based on a sequence of numbers, known as the Fibonacci sequence, where each number is the sum of the two preceding numbers. The sequence begins with 0, 1, 1, 2, 3, 5, 8, 13, 21, and so on.

 

To apply Fibonacci retracement to a security’s price movement, a trader would identify the high and low points of a move and divide the distance between these points into the key Fibonacci ratios of 23.6%, 38.2%, 50%, 61.8%, and 100%. These ratios are based on the percentage of a move that is retraced from the high or low point.

 

The resulting levels of retracement can then be plotted on a chart, creating a series of horizontal lines that indicate potential levels of support and resistance. Traders often use Fibonacci retracement levels in conjunction with other technical indicators to identify potential areas of trend reversal or continuation.

 

Average Directional Index (ADX)
The Average Directional Index (ADX) is a popular technical analysis tool used to measure the strength of a security’s trend. The indicator is based on the idea that the strength of a trend can be measured by the degree of separation between the security’s positive and negative directional indicators.

 

The ADX is calculated using the following steps:

 

Calculate the Directional Movement Index (DMI) for the security. The DMI is made up of the positive directional indicator (+DI) and the negative directional indicator (-DI), which are calculated based on the difference between the current high and the previous high and the difference between the current low and the previous low.

 

Calculate the directional index (DI) for each of the indicators. This is done by dividing the difference between the high and low by the total range for each period.

 

Calculate the smoothed directional index (DX) by taking the difference between the positive and negative directional indicators and dividing it by the sum of the positive and negative directional indicators.

 

Calculate the ADX by taking a moving average of the DX values over a specified period of time.

 

The ADX is typically displayed on a scale of 0 to 100, with readings above 25 indicating a strong trend and readings below 20 indicating a weak trend. Traders often use the ADX in conjunction with other technical indicators to confirm the strength of a current trend or identify potential trend reversals.
On-Balance Volume (OBV)
On-Balance Volume (OBV) is a technical analysis tool used to measure the momentum of a security’s price movement. The indicator is based on the idea that volume is a leading indicator of price movement and can help identify potential trend reversals or confirm the strength of a current trend.

 

The OBV is calculated by adding the volume on days where the security’s price increases and subtracting the volume on days where the price decreases. The resulting values are then added to the previous day’s OBV value to create a running total.

 

The OBV is typically displayed as a line chart that shows the cumulative total of the OBV values over time. Traders often use the OBV to identify potential trend reversals by looking for divergences between the OBV and the price of the security. If the OBV is increasing while the price is decreasing, this could be a sign of a potential uptrend reversal, and vice versa.

 

In addition to identifying potential trend reversals, the OBV can also be used to confirm the strength of a current trend. If the OBV is increasing along with the price of the security, this is a sign that the trend is likely to continue.
Ichimoku Kinko Hyo
Ichimoku Kinko Hyo, also known as the Ichimoku Cloud, is a popular technical analysis tool used to identify potential trend reversals and gauge the overall direction of a security’s price movement. The indicator is based on several lines that are plotted on a chart, creating a cloud-like formation that can be used to identify potential levels of support and resistance.

 

The indicator consists of the following components:

 

Tenkan-sen: The Tenkan-sen line is a fast-moving average that is calculated based on the highest high and lowest low over the past 9 periods.

 

Kijun-sen: The Kijun-sen line is a slower-moving average that is calculated based on the highest high and lowest low over the past 26 periods.

 

Senkou Span A: The Senkou Span A line is calculated by adding the Tenkan-sen and Kijun-sen lines and dividing the result by two. The resulting value is then plotted 26 periods ahead.

 

Senkou Span B: The Senkou Span B line is calculated based on the highest high and lowest low over the past 52 periods. The resulting value is then plotted 26 periods ahead.

 

Chikou Span: The Chikou Span line is the closing price of the security, plotted 26 periods behind the current price.

 

When the price of a security is above the cloud, this is a sign of a potential uptrend, and when the price is below the cloud, this is a sign of a potential downtrend. Traders also look for crossovers between the Tenkan-sen and Kijun-sen lines, as well as divergences between the Chikou Span line and the price of the security, to identify potential trend reversals.

History of Algos

Ancient Greece: 240BC
The Sieve Of Eratosthenes:A notable algorithm developed in this time, The sieve of Eratosthenes, is named after the ancient Greek mathematician Eratosthenes of Cyrene. Eratosthenes lived in the 3rd century BCE and was a polymath who made contributions to many fields, including mathematics, astronomy, geography, and philosophy.
Medieval Period: 1025 AD
Ibn Al-Haytham (Alhazen):In the Middle Ages, Islamic mathematicians developed algorithms, including ones for solving polynomial roots and arithmetic operations. Ibn al-Haytham (Alhazen) discovered the formula for the sum of fourth powers and developed a general formula for any integral powers, fundamental to integral calculus.
The Renaissance: 1500s
Kepler, Ferrari, And Cardano:European mathematicians of the Renaissance devised new algorithms for solving math problems, such as Johannes Kepler's planetary position algorithm. Lodovico de Ferrari assisted Gerolamo Cardano in solving quadratic and cubic equations, and solved many of the quadratic equations published by Cardano.
Industrial Revolution: 1800s
The First Computer Programmer:Modern algorithms began to take shape in the 19th century, thanks to the work of Ada Lovelace, a British mathematician. Lovelace was the first to realize the machine's potential beyond basic calculations and published the first algorithm intended for machine execution. Consequently, she's often hailed as the first computer programmer.
The Modern Era: 1900s
The Beginning Of Deep Learning:Backpropagation is a neural network algorithm that improves the accuracy of artificial neural networks by adjusting connection weights. It was created in 1974 and made neural networks more practical and fuelled the development of deep learning, which is now widely used in various fields.
The Age of Technology: 2000s
The Rise Of Algorithms:Algorithms for machine learning, data science, and cryptography have emerged in the 21st century. The exponential increase in algorithmic use is due to growth in computing resources and big data. As a result, algorithms have become essential in various fields like finance, healthcare, and transportation.
Black-Scholes-Merton: 2002
The Black-Scholes-Merton model, developed in 2002, is an algorithm for pricing options, a type of financial derivative. It is widely used in the finance industry and is based on the assumption that the price of an underlying asset follows a geometric Brownian motion.
High Frequency Trading: 2007
In the early 2000s, financial firms such as Renaissance Technologies, Tower Research Capital, and D. E. Shaw & Co developed high-frequency trading algorithms. These algorithms use supercomputers and mathematical models to quickly analyze market data and execute trades. Today, high-frequency trading is a major component of the financial industry.
Gradient Boosting: 2014
Gradient boosting machines (GBMs) are a machine learning algorithm used for predictive modeling and risk assessment. They build decision trees iteratively and are successfully applied in credit risk modeling. GBMs predict loan defaults and identify high-risk borrowers to provide accurate and reliable predictions, helping banks make better-informed decisions.
CNNs In Markets: 2015
Convolutional neural networks (CNNs) are used for stock market prediction by extracting relevant features through convolutional layers and processing them through fully connected layers to make predictions. CNNs outperform traditional machine learning algorithms in predicting stock prices. Further research aims to improve CNN accuracy and reliability in this field.
Reinforcement Learning: 2017
Reinforcement learning is a type of machine learning that focuses on decision-making processes. In capital markets, reinforcement learning can be used to optimize investment strategies by learning from past decisions and market trends. By using an algorithm that maximizes the long-term reward, reinforcement learning can help investors make more informed and profitable decisions.
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History of Algos

Ancient Greece: 240BC

The sieve of Eratosthenes

A notable algorithm developed in this time, The sieve of Eratosthenes, is named after the ancient Greek mathematician Eratosthenes of Cyrene. Eratosthenes lived in the 3rd century BCE and was a polymath who made contributions to many fields, including mathematics, astronomy, geography, and philosophy.

Medieval Period: 1025 AD

Ibn al-Haytham (Alhazen)

In the Middle Ages, Islamic mathematicians developed algorithms, including ones for solving polynomial roots and arithmetic operations. Ibn al-Haytham (Alhazen) discovered the formula for the sum of fourth powers and developed a general formula for any integral powers, fundamental to integral calculus.

The Renaissance: 1500s

Kepler, Ferrari, and Cardano

European mathematicians of the Renaissance devised new algorithms for solving math problems, such as Johannes Kepler's planetary position algorithm. Lodovico de Ferrari assisted Gerolamo Cardano in solving quadratic and cubic equations, and solved many of the quadratic equations published by Cardano.

The Industrial Revolution: 1800s

The first computer programmer

Modern algorithms began to take shape in the 19th century, thanks to the work of Ada Lovelace, a British mathematician. Lovelace was the first to realize the machine's potential beyond basic calculations and published the first algorithm intended for machine execution. Consequently, she's often hailed as the first computer programmer.

The Modern Era: 1900s

The Beginning of Deep Learning

Backpropagation is a neural network algorithm that improves the accuracy of artificial neural networks by adjusting connection weights. It was created in 1974 and made neural networks more practical and fuelled the development of deep learning, which is now widely used in various fields.

The Age of Technology: 2000s

The Rise of Algorithms

Algorithms for machine learning, data science, and cryptography have emerged in the 21st century. The exponential increase in algorithmic use is due to growth in computing resources and big data. As a result, algorithms have become essential in various fields like finance, healthcare, and transportation.

Black-Scholes-Merton Model: 2002

The Black-Scholes-Merton model, developed in 2002, is an algorithm for pricing options, a type of financial derivative. It is widely used in the finance industry and is based on the assumption that the price of an underlying asset follows a geometric Brownian motion.

High Frequency Trading Algos: 2007

In the early 2000s, financial firms such as Renaissance Technologies, Tower Research Capital, and D. E. Shaw & Co developed high-frequency trading algorithms. These algorithms use supercomputers and mathematical models to quickly analyze market data and execute trades. Today, high-frequency trading is a major component of the financial industry.

Gradient boosting machines: 2014

Gradient boosting machines (GBMs) are a machine learning algorithm used for predictive modeling and risk assessment. They build decision trees iteratively and are successfully applied in credit risk modeling. GBMs predict loan defaults and identify high-risk borrowers to provide accurate and reliable predictions, helping banks make better-informed decisions.

CNNs In Market Prediction: 2015

Convolutional neural networks (CNNs) are used for stock market prediction by extracting relevant features through convolutional layers and processing them through fully connected layers to make predictions. CNNs outperform traditional machine learning algorithms in predicting stock prices. Further research aims to improve CNN accuracy and reliability in this field.

The Use of Reinforcement Learning: 2017

Reinforcement learning is a type of machine learning that focuses on decision-making processes. In capital markets, reinforcement learning can be used to optimize investment strategies by learning from past decisions and market trends. By using an algorithm that maximizes the long-term reward, reinforcement learning can help investors make more informed and profitable decisions.

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