Using AI in Trading: How Computer Programs Decide When to Buy and Sell

[Shahrokh Firouzi]

Artificial intelligence (AI) is changing the way we buy and sell in the stock market. This discussion is about understanding how computers use AI to make quick trading decisions.

We will look into how AI can spot patterns in the market, make predictions, and decide when it’s a good time to trade. We’ll talk about the different ways AI learns about the market, including both learning from examples and figuring things out on its own. We’ll also discuss how AI can help traders manage risks and make better decisions.

Here are some questions to get us started:

1. How do AI systems use market data to decide when to buy or sell? This question will help us understand the steps AI takes to make trading decisions.

2. What makes AI trading different from traditional ways of trading? 

3. How can we trust AI to make trading decisions, and what can we do to make sure it’s making good choices? 

4. Can AI help us avoid losing money, and how? 

5. What should we think about when AI makes trading decisions? Are there any rules or guidelines we should follow? 

Hope we can continue this discussion and create a small project together. 

Waiting for some people to join.

[Sergey Golubev]

Some people say that AI is not an Ai, and it is logictic script (and it is too far from AI for now for any such as "AI").

• Some discussion about AI (and machine learning in trading) is going on this thread:
  Machine learning in trading: theory, models, practice and algo-trading (3753 pages in the thread; about coding, using, and so on, means: professional discussion; because almost all the participants wrote many articles here about machine learning and an AI for example).
  
• The next thread (just a discussion, thing about coding and trading): AI 2023. Meet ChatGPT. (212 pages in the thread)
• and there are some more.

So, it was already disccussed since 2016 (for information), and the discussing/coding/using/etc are continuing every day.

[Sergey Golubev]

Shahrokh Firouzi:
Here are some questions to get us started:

Yes, just to start -

------------------------

Machine Learning and Neural Network

Neural Network: discussion/development threads

1. Machine Learning and Neural Networks - key forum thread
2. Better NN EA development thread with indicators, pdf files and so on.
   
3. Better NN EA final thread 
4. taking NEURAL NETWORKS to the NEXT LEVEL - very interesting thread
5. Neural Networks thread (good public discussion)
6. How to build a NN-EA in MT4: usefull thread for developers.
7. Radial Basis Network (RBN) - As Fit Filter For Price: the thread 

Neural Network: Indicators and systems development

1. Self-trained MA cross!: development thread for new generation of the indicators
2. Levenberg-Marquardt algorithm: development thread

Neural Network: EAs

1. CyberiaTrader EA: discussion thread and EAs' thread.
2. Self learning expert thread with EAs' files here.
3. Artificial Intelligence EAs threads: How to "teach" and to use the AI ("neuron") EA thread and Artificial Intelligence  thread
4. Forex_NN_Expert EA and indicator thread.
5. SpiNNaker - A Neural Network EA thread. 

Neural Network: The Books

1. What to read and where to learn about Machine Learning (10 free books) - the post.

The article

• Neural Networks Made Easy - MT5
  
• Neural Network in Practice: Least Squares - MT5
  
• Neural Network in Practice: Straight Line Function - MT5
  
• Neural Network in Practice: Pseudoinverse (II) - MT5
  
• Neural Network in Practice: The First Neuron - MT5
• The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI - MT5
  
• Quantization in machine learning (Part 1): Theory, sample code, analysis of implementation in CatBoost - MT5
  
• Quantization in machine learning (Part 2): Data preprocessing, table selection, training CatBoost models - MT5
  
• ALGLIB numerical analysis library in MQL5 - MT5
  
• Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified - MT5
  
• Data Science and Machine Learning — Neural Network (Part 02): Feed forward NN Architectures Design - MT5
  
• Data Science and Machine Learning (Part 03): Matrix Regressions - MT5
• Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash - MT5
• Data Science and Machine Learning (Part 05): Decision Trees - MT5
• Data Science and Machine Learning (Part 06): Gradient Descent - MT5
• Data Science and Machine Learning (Part 07): Polynomial Regression - MT5
  
• Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5 - MT5
  
• Data Science and Machine Learning (Part 09) : The K-Nearest Neighbors Algorithm (KNN) - MT5
  
• Data Science and Machine Learning (Part 10): Ridge Regression - MT5
  
• Data Science and Machine Learning (Part 11): Naïve Bayes, Probability theory in Trading - MT5
  
• Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market? - MT5
  
• Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA) - MT5
  
• Data Science and Machine Learning (Part 14): Finding Your Way in the Markets with Kohonen Maps - MT5
  
• Data Science and Machine Learning (Part 15): SVM, A Must-Have Tool in Every Trader's Toolbox - MT5
  
• Data Science and Machine Learning (Part 16): A Refreshing Look at Decision Trees - MT5
  
• Data Science and Machine Learning (Part 17): Money in the Trees? The Art and Science of Random Forests in Forex Trading - MT5
  
• Data Science and Machine Learning (Part 18): The battle of Mastering Market Complexity, Truncated SVD Versus NMF - MT5
• Data Science and Machine Learning (Part 19): Supercharge Your AI models with AdaBoost - MT5
  
• Data Science and Machine Learning (Part 20) : Algorithmic Trading Insights, A Faceoff Between LDA and PCA in MQL5 - MT5
  
• Data Science and Machine Learning(Part 21): Unlocking Neural Networks, Optimization algorithms demystified - MT5
  
• Data Science and ML (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal - MT5
  
• Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models? - MT5
  
• Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models - MT5
  
• Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN) - MT5
  
• Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks - MT5
• Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It? - MT5
  
• Data Science and ML (Part 28): Predicting Multiple Futures for EURUSD, Using AI  - MT5
  
• Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes - MT5
  
• Data Science and ML(Part 30): The Power Couple for Predicting the Stock Market, Convolutional Neural Networks(CNNs) and Recurrent Neural Networks(RNNs) - MT5
• Data Science and ML (Part 31): Using CatBoost AI Models for Trading - MT5
  
• Data Science and ML (Part 32): Keeping your AI models updated, Online Learning - MT5
  
• Data Science and ML (Part 33): Pandas Dataframe in MQL5, Data Collection for ML Usage made easier - MT5
  
• Data Science and ML (Part 34): Time series decomposition, Breaking the stock market down to the core - MT5
  
• Data Science and ML (Part 35): NumPy in MQL5 – The Art of Making Complex Algorithms with Less Code - MT5
  
• Data Science and ML (Part 36): Dealing with Biased Financial Markets - MT5
• Experiments with neural networks (Part 1): Revisiting geometry - MT5
  
• Experiments with neural networks (Part 2): Smart neural network optimization - MT5
  
• Experiments with neural networks (Part 3): Practical application - MT5
• Experiments with neural networks (Part 4): Templates - MT5
  
• Experiments with neural networks (Part 5): Normalizing inputs for passing to a neural network - MT5
  
• Experiments with neural networks (Part 6): Perceptron as a self-sufficient tool for price forecast - MT5 
• Experiments with neural networks (Part 7): Passing indicators - MT5  
  
• Programming a Deep Neural Network from Scratch using MQL Language  - MT5
  
• Neural networks made easy (Part 2): Network training and testing - MT5
  
• Machine learning in Grid and Martingale trading systems. Would you bet on it? - MT5
  
• Neural networks made easy (Part 3): Convolutional networks - MT5
  
• Neural networks made easy (Part 4): Recurrent networks - MT5 
• Neural networks made easy (Part 5): Multithreaded calculations in OpenCL - MT5 
• Neural networks made easy (Part 6): Experimenting with the neural network learning rate - MT5 
• Neural networks made easy (Part 7): Adaptive optimization methods - MT5 
• Neural networks made easy (Part 8): Attention mechanisms - MT5
• Neural networks made easy (Part 9): Documenting the work - MT5
  
• Neural networks made easy (Part 10): Multi-Head Attention - MT5
  
• Neural networks made easy (Part 11): A take on GPT - MT5
  
• Neural networks made easy (Part 12): Dropout - MT5
  
• Neural networks made easy (Part 13): Batch Normalization - MT5
  
• Neural networks made easy (Part 14): Data clustering - MT5
  
• Neural networks made easy (Part 15): Data clustering using MQL5 - MT5
  
• Neural networks made easy (Part 16): Practical use of clustering - MT5
  
• Neural networks made easy (Part 17): Dimensionality reduction - MT5
  
• Neural networks made easy (Part 18): Association rules - MT5
  
• Neural networks made easy (Part 19): Association rules using MQL5 - MT5
  
• Neural networks made easy (Part 20): Autoencoders - MT5
  
• Neural networks made easy (Part 21): Variational autoencoders (VAE) - MT5
  
• Neural networks made easy (Part 22): Unsupervised learning of recurrent models - MT5
  
• Neural networks made easy (Part 23): Building a tool for Transfer Learning - MT5
  
• Neural networks made easy (Part 24): Improving the tool for Transfer Learning - MT5
  
• Neural networks made easy (Part 25): Practicing Transfer Learning - MT5
  
• Neural networks made easy (Part 26): Reinforcement Learning - MT5
  
• Neural networks made easy (Part 27): Deep Q-Learning (DQN) - MT5
  
• Neural networks made easy (Part 28): Policy gradient algorithm - MT5
  
• Neural networks made easy (Part 29): Advantage Actor-Critic algorithm - MT5
  
• Neural networks made easy (Part 30): Genetic algorithms - MT5
  
• Neural networks made easy (Part 31): Evolutionary algorithms - MT5
  
• Neural networks made easy (Part 32): Distributed Q-Learning - MT5
  
• Neural networks made easy (Part 33): Quantile regression in distributed Q-learning - MT5
  
• Neural networks made easy (Part 34): Fully Parameterized Quantile Function - MT5
• Neural networks made easy (Part 35): Intrinsic Curiosity Module - MT5
  
• Neural networks made easy (Part 36): Relational Reinforcement Learning - MT5
  
• Neural networks made easy (Part 37): Sparse Attention - MT5
  
• Neural networks made easy (Part 38): Self-Supervised Exploration via Disagreement - MT5
  
• Neural networks made easy (Part 39): Go-Explore, a different approach to exploration - MT5
• Neural networks made easy (Part 40): Using Go-Explore on large amounts of data - MT5
  
• Neural networks made easy (Part 41): Hierarchical models - MT5
  
• Neural networks made easy (Part 42): Model procrastination, reasons and solutions - MT5
  
• Neural networks made easy (Part 43): Mastering skills without the reward function - MT5
  
• Neural networks made easy (Part 44): Learning skills with dynamics in mind - MT5 
• Neural networks made easy (Part 45): Training state exploration skills - MT5 
• Neural networks made easy (Part 46): Goal-conditioned reinforcement learning (GCRL)  - MT5 
• Neural networks made easy (Part 47): Continuous action space - MT5 
• Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values  - MT5 
• Neural networks made easy (Part 49): Soft Actor-Critic - MT5
  
• Neural networks made easy (Part 50): Soft Actor-Critic (model optimization) - MT5
  
• Neural networks made easy (Part 51): Behavior-Guided Actor-Critic (BAC) - MT5
• Neural networks made easy (Part 52): Research with optimism and distribution correction - MT5
• Neural networks made easy (Part 53): Reward decomposition - MT5
• Neural networks made easy (Part 54): Using random encoder for efficient research (RE3) - MT5
• Neural networks made easy (Part 55): Contrastive intrinsic control (CIC) - MT5
  
• Neural networks made easy (Part 56): Using nuclear norm to drive research - MT5
  
• Neural networks made easy (Part 57): Stochastic Marginal Actor-Critic (SMAC) - MT5
• Neural networks made easy (Part 58): Decision Transformer (DT) - MT5
• Neural networks are easy (Part 59): Dichotomy of Control (DoC) - MT5
  
• Neural networks made easy (Part 60): Online Decision Transformer (ODT) - MT5
• Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning - MT5
  
• Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models - MT5
• Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT) - MT5
• Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method - MT5
• Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL) - MT5
  
• Neural networks made easy (Part 66): Exploration problems in offline learning - MT5
• Neural networks made easy (Part 67): Using past experience to solve new tasks - MT5
  
• Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization - MT5
  
• Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT) - MT5
• Neural networks made easy (Part 70): Closed-Form Policy Improvement Operators (CFPI) - MT5
• Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding GCPC) - MT5
  
• Neural networks made easy (Part 72): Trajectory prediction in noisy environments - MT5
• Neural networks made easy (Part 73): AutoBots for predicting price movements - MT5
  
• Neural networks made easy (Part 74): Trajectory prediction with adaptation  - MT5
• Neural networks made easy (Part 75): Improving the performance of trajectory prediction models - MT5
  
• Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer - MT5
  
• Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT) - MT5
• Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT) - MT5
  
• Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state - MT5
  
• Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN) - MT5
• Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR) - MT5
  
• Neural networks made easy (Part 82): Ordinary Differential Equation models (NeuralODE) - MT5
  
• Neural Networks Made Easy (Part 83): The "Conformer" Spatio-Temporal Continuous Attention Transformer Algorithm - MT5
  
• Neural Networks Made Easy (Part 84): Reversible Normalization (RevIN) - MT5
  
• Neural Networks Made Easy (Part 85): Multivariate Time Series Forecasting - MT5
  
• Neural Networks Made Easy (Part 86): U-Shaped Transformer - MT5
  
• Neural Networks Made Easy (Part 87): Time Series Patching - MT5
  
• Neural Networks Made Easy (Part 88): Time-Series Dense Encoder (TiDE) - MT5
  
• Neural networks made easy (Part 89): Frequency Enhanced Decomposition Transformer (FEDformer) - MT5
• Neural Networks Made Easy (Part 90): Frequency Interpolation of Time Series (FITS) - MT5
  
• Developing a self-adapting algorithm (Part I): Finding a basic pattern  - MT5
  
• Neural Networks Made Easy (Part 91): Frequency Domain Forecasting (FreDF) - MT5
  
• Neural Networks Made Easy (Part 92): Adaptive Forecasting in Frequency and Time Domains - MT5
• Neural Networks Made Easy (Part 93): Adaptive Forecasting in Frequency and Time Domains (Final Part) - MT5
  
• Neural Networks Made Easy (Part 94): Optimizing the Input Sequence - MT5
  
• Neural Networks Made Easy (Part 95): Reducing Memory Consumption in Transformer Models - MT5
  
• Neural Networks Made Easy (Part 96): Multi-Scale Feature Extraction (MSFformer) - MT5
  
• Neural Networks Made Easy (Part 97): Training Models With MSFformer - MT5
  
• Developing a self-adapting algorithm (Part II): Improving efficiency  - MT5
  
• Self-adapting algorithm (Part III): Abandoning optimization - MT5
  
• Deep neural network with Stacked RBM. Self-training, self-control - MT4 
• Practical application of neural networks in trading - MT5
  
• Practical application of neural networks in trading. Python (Part I) - MT5
  
• Practical application of neural networks in trading (Part 2). Computer vision - MT5
  
• Neural Networks in Trading: Piecewise Linear Representation of Time Series - MT5
  
• Neural Networks in Trading: Dual-Attention-Based Trend Prediction Model - MT5
  
• Neural Networks in Trading: Lightweight Models for Time Series Forecasting - MT5
  
• Neural Networks in Trading: Using Language Models for Time Series Forecasting - MT5
  
• Neural Networks in Trading: Practical Results of the TEMPO Method - MT5
  
• Neural Networks in Trading: A Complex Trajectory Prediction Method (Traj-LLM) - MT5
  
• Neural Networks in Trading: Unified Trajectory Generation Model (UniTraj) - MT5
  
• Neural Networks in Trading: Hierarchical Vector Transformer (HiVT) - MT5
  
• Neural Networks in Trading: Hierarchical Vector Transformer (Final Part) - MT5 
• Neural Networks in Trading: Hierarchical Feature Learning for Point Clouds - MT5
• Neural Networks in Trading: Point Cloud Analysis (PointNet) - MT5
  
• Neural Networks in Trading: Transformer for the Point Cloud (Pointformer) - MT5
  
• Neural Networks in Trading: Scene-Aware Object Detection (HyperDet3D) - MT5
  
• Neural Networks in Trading: Exploring the Local Structure of Data - MT5
• Connecting NeuroSolutions Neuronets - MT5
• Using Neural Networks In MetaTrader - MT4
• Price Forecasting Using Neural Networks - MT4
• Recipes for Neuronets - MT4
• Third Generation Neural Networks: Deep Networks - MT5
• Neural Networks Cheap and Cheerful - Link NeuroPro with MetaTrader 5 - MT5
• Creating Neural Network EAs Using MQL5 Wizard and Hlaiman EA Generator - MT5
• Neural network: Self-optimizing Expert Advisor - MT5
• Neural Networks: From Theory to Practice - MT5
• Using MetaTrader 5 Indicators with ENCOG Machine Learning Framework for Timeseries Prediction - MT5
• Using Self-Organizing Feature Maps (Kohonen Maps) in MetaTrader 5 - MT5
• Deep Neural Networks (Part I). Preparing Data - MT5
• Deep Neural Networks (Part II). Working out and selecting predictors - MT5
  
• Mastering Model Interpretation: Gaining Deeper Insight From Your Machine Learning Models - MT5

CodeBase

• Next price predictor using Neural Network - indicator for MetaTrader 4
• Easy Neural Network - library for MetaTrader 5
  
• LGLIB - Numerical Analysis Library - library for MetaTrader 4
• ALGLIB - Numerical Analysis Library - library for MetaTrader 5
  
• MTS Neural network plus MACD - expert for MetaTrader 4
• ArtificialIntelligence_Right - expert for MetaTrader 4
• NeuroNirvamanEA - expert for MetaTrader 4
• Create your own neural network predictor easily (example: MA and RSI Predictors) - indicator for MetaTrader 4
• Automated Trading System "Сombo" - expert for MetaTrader 4
• MTC Neural network plus MACD - expert for MetaTrader 5
• Bollinger Band Width calculation with Neural Network using - expert for MetaTrader 5
• PNN Neural Network Class - library for MetaTrader 5
• GRNN Neural Network Class - library for MetaTrader 5
• RBF Neural Network Class - library for MetaTrader 5
• MLP Neural Network Class - library for MetaTrader 5
• Artificial Intelligence - expert for MetaTrader 5

[Lorentzos Roussos]

Shahrokh Firouzi:

Artificial intelligence (AI) is changing the way we buy and sell in the stock market. This discussion is about understanding how computers use AI to make quick trading decisions.

We will look into how AI can spot patterns in the market, make predictions, and decide when it’s a good time to trade. We’ll talk about the different ways AI learns about the market, including both learning from examples and figuring things out on its own. We’ll also discuss how AI can help traders manage risks and make better decisions.

Here are some questions to get us started:

1. How do AI systems use market data to decide when to buy or sell? This question will help us understand the steps AI takes to make trading decisions.

2. What makes AI trading different from traditional ways of trading? 

3. How can we trust AI to make trading decisions, and what can we do to make sure it’s making good choices? 

4. Can AI help us avoid losing money, and how? 

5. What should we think about when AI makes trading decisions? Are there any rules or guidelines we should follow? 

Hope we can continue this discussion and create a small project together. 

Waiting for some people to join.

1. You give the system examples of successful trades and it tries to mimic the "function" that decides on those trades.think of it this way , you provide it with the desired outcome and with everything you knew at that point in time for that outcome before the outcome . It tries to create a giant equation that results in trading or not.It's mathematical programming essentially.
2. It will adapt when you provide more examples or as it collects examples live.
3. You can't make sure . The latent space the training will create may have some unwanted reactions in it , all within the buy sell close scope of course . In order to anticipate everything you must "browse" the entirety of the latent space . If you could do that however you would also have the processing power available to brute force the solution in the first place.(or hack into satoshi nakamoto's wallet)

4+5 no comment . 

The best place to start is python . 

There is also pytorch lightning and aws because you will need a lot of horse power.

[Lorentzos Roussos]

Some simplifications :

the neural networks have 2 ends , the input side and the output side .

How you train them is you provide a list of observations to the input side and a list or one item that was the outcome to the output side.

It then tries to adjust all connections between the 2 sides so that when you provide observations to the input side it will forecast the list of outcomes to the output side.

for instance .

you give it the list of observations 2 ,2 and the outcome 4

It could be 2*2 or 2+2 it does not know

you then give it another example 3,3 and the outcome 6

it then get's clearer . etc.

But imagine the list of observations in your case would be indicator values BEFORE the trade and on the output side the desired decision (buy , sell , nothing)

It is important for the list of observations provided to have been possible in the past . I've seen a guy trying to guess the outcome of football matches by using the stats of the completed football match. so caution there.

It has to be said however that training a neural network is the equivalent of scoring a 3 point shot from a different stadium.That's where the "horse power" requirements come in .You need to be burning through failed iterations fast 

Another aspect is for a given problem we do not know what the equivalency of 2,2=4 and 3,3=6 is for the problems we are trying to solve . In the example we know we are supposed to add and how "fundamentally" different 2,2 and 3,3 are . But in a problem we want to solve it is like sailing in uncharted territory

[Evren Caglar]

Shahrokh Firouzi:

Artificial intelligence (AI) is changing the way we buy and sell in the stock market. This discussion is about understanding how computers use AI to make quick trading decisions.

We will look into how AI can spot patterns in the market, make predictions, and decide when it’s a good time to trade. We’ll talk about the different ways AI learns about the market, including both learning from examples and figuring things out on its own. We’ll also discuss how AI can help traders manage risks and make better decisions.

Here are some questions to get us started:

1. How do AI systems use market data to decide when to buy or sell? This question will help us understand the steps AI takes to make trading decisions.

2. What makes AI trading different from traditional ways of trading? 

3. How can we trust AI to make trading decisions, and what can we do to make sure it’s making good choices? 

4. Can AI help us avoid losing money, and how? 

5. What should we think about when AI makes trading decisions? Are there any rules or guidelines we should follow? 

Hope we can continue this discussion and create a small project together. 

Waiting for some people to join.

I agree with the view that AI is not an AI. AI is just a fashion brand to improve sales. 

I would not add much meaning into AI.

We have better trading algorithms for retail users these days. But it is not because of AI, it is because there are better quality developers around.

Everything fall into a small thing which is a working good quality trade setup and programming.

You all need is a good reasoning behind a trade idea and an efficient trade management and quality programmer to code the behavior of that trade algorithm. This is what people call AI which is nothing but coding.

[Tobias Johannes Zimmer]

In an interactive machine learning course I picked up the information that the invention of perceptrons goes back 70 or 80 years. But it was first proved to work with match boxes that held colored plastic pearls by a mathematician. When the scientist wanted to ensure to pick a red pearl instead of blue for example, they had to put more red pearls into the box. This is roughly how a learning rate changes the weights in a neural net. But it gets complicated quickly. You can use a net with no hidden layer to simulate AND or OR conditions by playing with the weights and biases. Depending on these you can activate the output depending if one or more neutons are needed. With a high number of training runs the desired outcome is going to be approximated automatically. Or suppose you would like to model a straight line using four neurons (two points with both x and y coordinates).

But for an XOR condition you would need one hidden layer to activate neuron A AND NOT B. This is very interesting and it shows that after a hundred neurons and a few layers it gets really unintuitive, except maybe if you find an architecture where multiple nets work together. Maybe one for the long signal, short signal, lotsize, sl, tp etc.

[Rajesh Kumar Nait]

Shahrokh Firouzi:

Artificial intelligence (AI) is changing the way we buy and sell in the stock market. This discussion is about understanding how computers use AI to make quick trading decisions.

We will look into how AI can spot patterns in the market, make predictions, and decide when it’s a good time to trade. We’ll talk about the different ways AI learns about the market, including both learning from examples and figuring things out on its own. We’ll also discuss how AI can help traders manage risks and make better decisions.

Here are some questions to get us started:

1. How do AI systems use market data to decide when to buy or sell? This question will help us understand the steps AI takes to make trading decisions.

2. What makes AI trading different from traditional ways of trading? 

3. How can we trust AI to make trading decisions, and what can we do to make sure it’s making good choices? 

4. Can AI help us avoid losing money, and how? 

5. What should we think about when AI makes trading decisions? Are there any rules or guidelines we should follow? 

Hope we can continue this discussion and create a small project together. 

Waiting for some people to join.

I do not believe that AI is changing the way as of now but it may change the way in future.

As of now AI is only providing codes with error which may be a little bit time saving for developer only.

I believe quantum computer may change the way but its unavailable to public.

Successful trading is all about problem solving in big data. Manually its very rare to find a strategy that performs well in all condition of market

A quantum computer may bring solution by studying big data more accurately than AI as of now

[Conor Mcnamara]

Rajesh Kumar Nait #:

I do not believe that AI is changing the way as of now but it may change the way in future.

As of now AI is only providing codes with error which may be a little bit time saving for developer only.

I believe quantum computer may change the way but its unavailable to public.

Successful trading is all about problem solving in big data. Manually its very rare to find a strategy that performs well in all condition of market

A quantum computer may bring solution by studying big data more accurately than AI as of now

Well what about these "AI" marketed expert advisors then?  I haven't tested them on live, so I don't know if they're fraudulent or not. But I certainly would stay away if there are no reviews at all. A flawless backtest could be the great big scam.

[Adekunle Michael Olanrewaju]

Shahrokh Firouzi:

Artificial intelligence (AI) is changing the way we buy and sell in the stock market. This discussion is about understanding how computers use AI to make quick trading decisions.

We will look into how AI can spot patterns in the market, make predictions, and decide when it’s a good time to trade. We’ll talk about the different ways AI learns about the market, including both learning from examples and figuring things out on its own. We’ll also discuss how AI can help traders manage risks and make better decisions.

Here are some questions to get us started:

1. How do AI systems use market data to decide when to buy or sell? This question will help us understand the steps AI takes to make trading decisions.

2. What makes AI trading different from traditional ways of trading? 

3. How can we trust AI to make trading decisions, and what can we do to make sure it’s making good choices? 

4. Can AI help us avoid losing money, and how? 

5. What should we think about when AI makes trading decisions? Are there any rules or guidelines we should follow? 

Hope we can continue this discussion and create a small project together. 

Waiting for some people to join.

nice

[Gamuchirai Zororo Ndawana]

I started learning about AI to test the theory in the market. AI can learn to trade effectively, the key is to transform the data. I've said too much.