Algorithms are sets of rules for solving problems or accomplishing tasks.

Cracks in the Pipeline Part Two: High Frequency Trading

Global Algorithmic Trading Market to Surpass US$ 21,685.53 Million by 2026

Table of Contents
What Is an Algorithm?
Understanding Algorithm
Types of Algorithmic Trading
Algorithm Trading Example
Algorithms in Computer Science Also referred to as automated trading or black-box trading, algo trading uses computer programs to buy or sell securities at a pace not possible for humans. Algorithmic trading, also known as automated trading or black-box trading, uses a computer program to buy or sell securities at a pace not possible for humans. In computer science, a programmer must employ five basic parts of an algorithm to create a successful program:
1.  describe the problem in mathematical terms
2.  create the formulas and processes that create results
3.  input the outcome parameters
4.  execute the program repeatedly to test its accuracy
5.  conclusion of the algorithm is the result given after the parameters go through the set of instructions in the program.
For financial algorithms, the more complex the program, the more data the software can use to make accurate assessments to buy or sell securities. For example, algorithmic trading, known as algo trading, is used for deciding the timing, pricing, and quantity of stock orders.

Algorithm

Algorithmic trading is a system that utilizes very advanced mathematical models for making transaction decisions in the financial markets. 

Release No. 34-59593; File No. NYSEALTR-2009-28

High-Frequency Trading: Reaching the Limits

The Cost of Algorithmic Trading: A First Look at Comparative Performance

Judgement Day: Algorithmic Trading Around the Swiss Franc Cap Removal

Algorithmic trading is a process for executing orders utilizing automated and pre-programmed trading instructions to account for variables such as price, timing and volume. According to research, algorithmic trading is especially beneficial for large order sizes that may comprise as much as 10% of overall trading volume.﻿﻿ Typically market makers use algorithmic trades to create liquidity. Author Michael Lewis brought high-frequency, algorithmic trading to the public’s attention when he published the best-selling book _Flash Boys_, which documented the lives of Wall Street traders and entrepreneurs who helped build the companies that came to define the structure of electronic trading in America. Algorithmic trading is a process for executing orders utilizing automated and pre-programmed trading instructions to account for variables such as price, timing and volume. Algorithmic trading can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies.

Algorithmic Trading

An application programming interface, or API, is a "go-between" that enables a software program to interact with other software.

An application programming interface (API) is a set of programming code that queries data, parses responses, and sends instructions between one software platform and another. In the domain of financial markets and trading, one may use an API to establish a connection between a set of automated trading algorithms and the trader's preferred trading broker platform for the purpose of obtaining real-time quotes and pricing data, or to place electronic trades. There are two types of traders that use broker APIs:
**Third-Party Applications** - Many traders use third-party applications that require access to broker APIs for pricing data and the ability to place trades. Many brokers provide online documentation for their APIs, where developers can find out exactly how to authenticate with the API, what data is available for consumption, how to place orders through the API, and other technical details. **Developer Applications** - A growing number of traders develop their own automated trading systems, using programming languages like Python, and require a way to access pricing data and place trades.

Application Programming Interface (API)

Arbitrage pricing theory is a pricing model that predicts a return using the relationship between an expected return and macroeconomic factors.

E(R) i \= E ( R ) z \+ ( E ( I ) − E ( R ) z ) × β n where: E(R) i \= Expected return on the asset R z \= Risk-free rate of return β n \= Sensitivity of the asset price to macroeconomic factor  n E i \= Risk premium associated with factor  i \\begin{aligned} &\\text{E(R)}\_\\text{i} = E(R)\_z + (E(I) - E(R)\_z) \\times \\beta\_n\\\\ &\\textbf{where:}\\\\ &\\text{E(R)}\_\\text{i} = \\text{Expected return on the asset}\\\\ &R\_z = \\text{Risk-free rate of return}\\\\ &\\beta\_n = \\text{Sensitivity of the asset price to macroeconomic} \\\\ &\\text{factor}\\textit{ n}\\\\ &Ei = \\text{Risk premium associated with factor}\\textit{ i}\\\\ \\end{aligned} ​E(R)i​\=E(R)z​+(E(I)−E(R)z​)×βn​where:E(R)i​\=Expected return on the assetRz​\=Risk-free rate of returnβn​\=Sensitivity of the asset price to macroeconomicfactor nEi\=Risk premium associated with factor i​
The beta coefficients in the APT model are estimated by using linear regression. Arbitrage pricing theory (APT) is a multi-factor asset pricing model based on the idea that an asset's returns can be predicted using the linear relationship between the asset’s expected return and a number of macroeconomic variables that capture systematic risk. Arbitrage pricing theory (APT) is a multi-factor asset pricing model based on the idea that an asset's returns can be predicted using the linear relationship between the asset’s expected return and a number of macroeconomic variables that capture systematic risk. Gross domestic product (GDP) growth: _ß_ = 0.6, RP = 4%
Inflation rate: _ß_ = 0.8, RP = 2%
Gold prices: _ß_ = -0.7, RP = 5%
Standard and Poor's 500 index return: _ß_ = 1.3, RP = 9%
The risk-free rate is 3%
Using the APT formula, the expected return is calculated as:
Expected return = 3% + (0.6 x 4%) + (0.8 x 2%) + (-0.7 x 5%) + (1.3 x 9%) = 15.2%

of Arbitrage Pricing Theory (APT)

An arbitrage trading program (ATP) is a computer program that seeks to profit from financial market arbitrage opportunities.

An arbitrage trading program (ATP) is a computer program that seeks to profit from financial market arbitrage opportunities. Risk arbitrage (risk arb), also known as merger arbitrage, is an investment strategy to profit from the narrowing of a gap of the trading price of a target's stock and the acquirer's valuation of that stock in an intended takeover deal. Institutional traders or market makers have several advantages over retail traders in relation to arbitrage trading, including faster news sources, high-performance computers, and more sophisticated arbitrage trading software programs. An arbitrage trading program (ATP) is a computer program that seeks to profit from financial market arbitrage opportunities. High-frequency traders are a subset of arbitrage and program trading, as these traders attempt to profit from order flow, very quickly, resulting in arbitrage-like opportunities.

Arbitrage Trading Program (ATP)

Attribute bias is the tendency of stocks selected by a quantitative technique or model to have similar fundamental characteristics.

Attribute bias is a characteristic of quantitative techniques or economic models whereby they tend to choose investment instruments that have similar fundamental characteristics. While attribute bias refers to a bias in the methodology of picking financial instruments for a portfolio, self-attribution bias refers to a bias a person can have that causes them to think that the success they have in business, choosing investments, or other financial situations is because of their own personal characteristics. One way to correct for attribute bias and choose a balanced portfolio is simply to use several different models to choose securities and use different parameters for each model. Each model can have attribute bias, but since the investor has balanced the parameters of the different models, the portfolio will be balanced even if each smaller subset of securities is not. The danger in choosing a portfolio using a model with attribute bias is that the portfolio may contain similar securities, which can amplify market downturns.

Attribute Bias

Autotrading is a trading plan based on buy and sell orders that are automatically placed based on an underlying system or program.

Autotrading is a trading plan where buy and sell orders are automatically placed based on an underlying system or program. Rules need to be simple enough to be coded, and can't include subjectivity, as the computer needs defined rules.
Things to consider include, but are not limited to:
Position size, and how it will be defined;
How trades will be entered, and what specific parameters will trigger a trade;
How trades will be closed, and what triggers the closing of a trade;
Constraints on the system, such as when it should or shouldn't trade;
Need for safeguards.
These topics are elaborated on in more detail below. Or it could be more advanced, first defining the difference between the entry price and stop loss, setting a maximum risk, such as 1% of the account capital, and then defining the position size based on the 1% risk and the distance between the stop loss and entry on the particular trade. Institutional investors and technical traders will use complex systems that allow for conditional orders and strategies such as grid trading, trend trading, scalping, or fading. Many technical day traders will only work with brokers that allow plug-ins or external programs to connect to their platform, or that offer a coding program within the platform itself to create indicators and autotrading programs.

Autotrading

Entropy is a quantitative measure of randomness. The concept of entropy is used by financial analysts and market technicians to determine the probabilities that specific types of price action predicted of a security or market will come to fruition. Like the concept of noise, entropy is used to help model and represent the degree of uncertainty of a random variable, such as the prices of securities, in a market. In other words, entropy is used as a way to identify the best variable to define risk within a given system or financial instrument arrangement. The concept of entropy can be applied and represented by a variable to eliminate the randomness created by the underlying security or asset, which allows the analyst to isolate the price of the derivative.

Entropy

Fine tuning refers to the process of making small modifications to improve or optimize an outcome. 

In professional finance, fine tuning often refers to the process of making small modifications and improvements to investing strategy. Another form of fine tuning is not associated with coding or electronic algorithms, but is known In the investment industry as strategic investment fine tuning. Developments around the use of fine tuning in fuzzy logic\-based, artificial-intelligence, neural-network, or genetic algorithms, provide a few examples of areas which may trigger a desire for fine tuning in algorithmic development. Technical analysts using a variety of trading strategies can also fine tune their trading plans by identifying and using new technical analysis patterns or more comprehensively combining technical analysis patterns systematically to create stronger conviction around trading signals. In recent years the rise of algorithmic trading and quantitative investing has given the notion of fine tuning new dimensions.

Fine Tuning

Fuzzy logic is a mathematical logic that solves problems with an open, imprecise data spectrum. Read how to obtain accurate conclusions with fuzzy logic.

"Fuzzy Logic in Artificial Intelligence: Architecture, Applications, Advantages & Disadvantages." 

Table of Contents
What Is Fuzzy Logic? Understanding Fuzzy Logic
History of Fuzzy Logic
Special Considerations
Fuzzy Semantics
Examples of Fuzzy Logic
Pros and Cons of Fuzzy Logic
The Bottom Line
Fuzzy Logic FAQs
Fuzzy Logic is an approach to variable processing that allows for multiple possible truth values to be processed through the same variable. Fuzzy Logic Advantages
More likely to reflect real-world problems than classical logic.
Fuzzy logic algorithms have lower hardware requirements than classical boolean logic. Instead of requiring all statements to be absolutely true or absolutely false, as in classical logic, the truth values in fuzzy logic can any value between zero and one. Fuzzy logic is a set of rules that can be used to reach logical conclusions from fuzzy sets of data.

Fuzzy Logic

Neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works.

An Innovative Neural Network Approach for Stock Market Prediction

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. A neural network works similarly to the human brain’s neural network. A neural network evaluates price data and unearths opportunities for making trade decisions based on the data analysis. Neural networks, in the world of finance, assist in the development of such process as time-series forecasting, algorithmic trading, securities classification, credit risk modeling and constructing proprietary indicators and price derivatives.

Neural Network

A package deals allow traders to ensure specific prices or times to maturity for multiple assets.

A package deal is an order or transaction that contains a number of smaller exchange or transaction items that must be completed simultaneously, or not at all. In IDAL's package deal contract, an investor is promised incentives including:
Full exemption from corporate income tax for a period that can run up to 10 years, project dividends taxes for a period that can run up to 10 years, and land registration fees;
Up to 50% reduction on work, residence and construction permit fees;
Securing work permits with no delay
Another example of a package deal was Softbank's 2017 agreement to invest in Uber Technologies, as reported by TechCrunch. For a package deal to work, all specifications of the trades in the package must be executed; otherwise, it does not trade. A package deal outside of financial trading can also refer to a project or an agreement that involves multiple related items or offers that constitute multiple benefits. Because it is a package deal, if all specifications of the trade are not executed, then the deal never happens.

Package Deal

Program trading refers to the use of computer-generated algorithms to make trades in large volumes and sometimes with great frequency.

Table of Contents
What Is Program Trading?
Understanding Program Trading
Program Trading Purpose
Program Trading Example NYSE defines program trading as the purchase or sale of a group of 15 or more stocks that have a total market value of $1 million or more and are part of a coordinated trading strategy. In 2021, program trading is estimated to account for 70% to 80% of all U.S. stock market trades placed during a typical trading day, with that number rising to above 90% during periods of extreme volatility. As of 2021, program trading is estimated to account for 70% to 80% of all U.S. stock market trades placed during a typical trading day, with that number rising to above 90% during periods of extreme volatility. A program trading algorithm can look at the portfolio equity and quickly execute all the trades at once, buying the stocks that are under-allocated and selling the ones that are over-allocated to rebalance the portfolio in seconds.

Program Trading

Quantitative trading consists of trading strategies that rely on mathematical computations and number-crunching to identify trading opportunities.

Quantitative trading consists of trading strategies based on quantitative analysis, which rely on mathematical computations and number crunching to identify trading opportunities. Quantitative trading consists of trading strategies based on quantitative analysis, which rely on mathematical computations and number crunching to identify trading opportunities. Quantitative trading utilizes mathematical functions and automated trading models to make trading decisions. Quantitative trading techniques are utilized extensively by certain hedge funds, high-frequency trading (HFT) firms, algorithmic trading platforms, and statistical arbitrage desks. A disadvantage of quantitative trading is that it has limited use: a quantitative trading strategy loses its effectiveness once other market actors learn of it, or as market conditions change.

Quantitative Trading

Quantitative analysis (QA) seeks to understand behavior by using mathematical and statistical modeling, measurement, and research.

Quantitative analysis (QA) is a technique that uses mathematical and statistical modeling, measurement, and research to understand behavior. It is common for a company to use quantitative analysis to evaluate figures such as sales revenue, profit margins, or return on assets (ROA).
However, to get a better picture of a company's performance, analysts also evaluate information that is not easily quantifiable or reduced to numeric values, such as reputation or employee morale. Qualitative analysis focuses on meanings, involves sensitivity to context rather than the desire to obtain universal generalizations, and establishes rich descriptions rather than quantifiable metrics. Quantitative analysis is applied to the measurement, performance evaluation, valuation of a financial instrument, and predicting real-world events such as changes in a country's gross domestic product (GDP). Quantitative analysis (QA) is a technique that uses mathematical and statistical modeling, measurement, and research to understand behavior. 2:11
Quantitative analysis (QA) is a technique uses mathematical and statistical modeling, measurement, and research that to understand behavior.

Automated Trading Basic Education

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