Application to portfolio management, trading strategies, and fraud detection.
Algorithms are the Basic Blocks of Programming.
They’ll serve as a basis for you to choose the Patterns & Networks you should use.
As their role is critical, you want to know them, even if you don’t manipulate them directly.
In this article we’ll focus on 3 main concerns : data analysis, risk assessment, and related decision-making. As well as their application to portfolio management, trading strategies, and fraud detection.
Don’t hesitate to give us your feedback on this list and the groups made through this article.
Group 1: Risk Assessment and Portfolio Optimization
Wherever you are a programmer or a financial advisor Risk & Optimization are your 2 priorities.
The Monte Carlo Simulation is a popular algorithm in finance for risk assessment and portfolio optimization. It generates simultaneous simulations based on variables you defined according to your Industry/Domain.
The Black-Scholes-Merton Model is an algorithm used in option pricing and risk management. It approximates the price of financial derivatives, considering their underlying asset price, volatility, time to expiration, and interest rates.
If you code trading assistants consider both of these algorithms as a basis for your portfolio management.
Group 2: Predictive Modeling and Analysis
These algorithms are used in traditional Machine Learning.
Support Vector Machines are employed in finance for credit scoring, fraud detection, and stock market forecasting.
They are meant for supervised learning as they can classify/predict outcomes based on labeled training data.
Random Forest is used for credit risk assessment and stock price prediction.
It combines multiple decision trees to enhance predictions accuracy by aggregating the results from each tree.
Genetic Algorithms are applied in finance for portfolio optimization and trading strategy development.
These algorithms refine solutions over multiple iterations in a more versatile way than the former 2.
I present them as Optimization and Strategy Development tools as I think they’d suit best beginner programmers or financial professionals in this regard.
They can be used for most tasks described in this article.
Group 3: Sequential Data Analysis
Hidden Markov Models are used for stock price modeling and algorithmic trading.
They are probabilistic models Getting the underlying structure in information sequences.
Long Short-Term Memory Networks are a type of recurrent neural network used in finance for stock price prediction and algorithmic trading.
They can Get long-term dependencies in information sequences.
They’re meant for time series analysis and modeling complex financial patterns.
They seem to work best when associated with Behavioral Design Patterns when used in combination with General Adversarial Networks.
I typically separate general Algorithms and Networks clearly.
In this instance, presenting Markov Models & Long-Short Term Memory seemed essential for Time Series analysis which will be developed in latter articles.
Group 4: Data Analysis and Pattern Recognition
K-means Clustering is utilized in finance for tasks like customer segmentation and fraud detection.
It groups similar data points together based on their characteristics, enabling effective risk assessment, targeted marketing strategies, and anomaly detection.
Gradient Boosting algorithms are used for credit scoring, fraud detection, and portfolio optimization.
They combining weak learners in a sequential manner to enhance predictive accuracy.
The 2 most common examples of Gradient Boosting are eXtreme Gradient Boost and Light Gradient Boosting Machine.
Group 5: Association and Recommendation Systems
The Apriori Algorithm is employed for tasks market basket analysis and recommendation systems.
It identifies frequent item sets and association rules in transactional information, enabling insights into customer behavior, cross-selling opportunities, and personalized recommendations.
