Hello, I am basic. Self taught person for everything I do. Especially the last decade to make a living day trading. I wanted to share the wealth and so I I have tired to develop 100 different ways to teach people to trade like me. I even did it for free. Stupid I know. They all quit too. But I still truly believe having more competitive access to the stock market will give our people more $$ to live off of and in the now. Instead of being promised crumbs from a 401 or savings plan that they may or may not live long enough to enjoy.
So That being said I will self build the full stack as well. But I really could use some help from people who know what their doing. Im also learning after a ton of tiral and error on all the Cloud, Ai ML systems that most of this is built for large amounts of collected data or customer related data etc etc. Well What I need is a place to produce an active Ai that’s I guess the word is inference from the ML Trained Neural Network. Basically Ive figured out the automation side. Thats easy. I even figured out a way to open a ton of think or swim instances and made them all just mimic my every mouse click. But now I need to build this brain to think like me and other pro traders. and then create a front end solution thats running that Ai every time a client activates thei token to trade on their choice in trading software.
Im curious if just Jupiter note booking it and building it all from scratch or is something like IBM or Hashicorp or Glcoud or aws. Are these solutions to build what I need.
Also is there anything trained out there to learn the streaming visualizations trading platforms produce? I like that pixel distances thing. I think thats my gig. Every action I make is done by visually processing the charts… Thats it… I dont need fundamental data, However it will help train these bots to be be fully autonomous.
Sorry for my lack of knowledge. I am dying to have a regular conversation with a data scientist.
hello.
It sounds like you’re doing something very advanced. I don’t do it.
In my case, I am making a model that can predict injury and illness prediction data such as COVID, MonkeyPox, and power consumption with artificial intelligence.
In the case of economic models, I think we will train artificial intelligence based on Bayesian probability models. However, in my professional category, it is a data model based on Fisher statistics and based on physical causality, so it is easy to say that it is very predictable.
We hope that you will use Anaconda for AI model creation and ML model verification.
Hi! Thanks for the reply. It took me a little time to read your message and try to process it. I am very new to the vocabulary. But I think I follow. You were explaining to me some of the models you use for predictions? What your doing for covid ML models is a form of back propagation and then your forward propagating the infrence from the ML results?
I think one of the biggest issues that I’m facing is trying to create my own Vision Ai that overlays and reads the stock charts, theres a great formula out there that reads the pixels. I think this is the best route. I guess I really have no idea how to program so I’m having to do things by copying and pasting a string of code at a time. So trying to understand the environments and build those out. It took me a while to figure out I was gonna have to create my own Ai and ML from scratch after spending days on the the top cloud services out there just to find out how hindered I am by their setup. As easy as it is to create a instance and get to ML projects is super fast and easy. But its not what I need until Im ready to scale my finished Application.
Thank for your time and would enjoy any topic of conversation with machine learning or Artificial Intelligence.
You were explaining to me some of the models you use for predictions? What your doing for covid ML models is a form of back propagation and then your forward propagating the infrence from the ML results?
In my case, in ML, I use the result of backpropagation in the hyperparameter settings for forward propagation.
In terms of electronics, it is exactly the same as PLL (Phase Lock Loop).
In the case of this model, it has the characteristic of being resistant to disturbance, and it becomes a system in which some action follows the prediction result.
In other words, it can be called an ML that shows the action for the goal. This is because when presenting predictive models to policy makers and governments, it is necessary to explain those factors.
Thank you for using Anaconda. We hope that your AI system will continue to work well in the future.
Omg that was amazing to read! Fascinating. So I wanna show you something if you don’t mind. I ended up finding a company that’s doing a couple of the things that I’m trying to do. Pretty much all the visual side. If you look at some of the pictures of the charting overlays, it shows the visual predictions for which ever “study/tools” the user uses for technical analysis to predict price movement normally.
But the one thing they still lack is the fully autonomous feature. Which is building out an elaborate highly trained Neural Network to cognitively implement my logic to trade like me and other professionals. Then giving access to average people who don’t know how to trade or dont have the time to learn or trade. Right now it looks like their product is more for experienced users and its a separate platform. .
So I’m still in luck its looks like and the packaging of my future product is still different and more advanced. sounds like what your doing in terms of giving pre-predictions explaining the decisions before the inference is whaf this websites/app I am sending you a link for is doing visually… do you know of any forums kr places I can go to help learn how to build the environments for Ai like in this link?
Thank you so much for answering and humoring my inexperience.
Hey, first of all, it’s awesome that you’re self-teaching and working so hard on developing your trading system. I think what you’re trying to build could be very powerful, and it sounds like you’re already on the right track by looking into AI and machine learning for your trading strategies.
To address some of your questions:
Building the AI: You’re right that cloud platforms like AWS, GCP (Google Cloud Platform), and IBM provide infrastructure to run machine learning models at scale. They have great tools for training and deploying AI models, especially if you’re dealing with large amounts of data. If you’re looking for a more hands-on approach, using Jupyter notebooks is a good way to experiment and build prototypes. Once you’ve refined your models, you can scale them on these cloud platforms for production use. The big advantage of using these platforms is that they provide managed services, like AI/ML pipelines and tools for deploying models, which can save you time in the long run.
Inference from a Neural Network: It sounds like you’re focusing on the inference side of things – where the trained model makes decisions in real time based on new data. This is what you would typically deploy to run trading algorithms in a live environment. Platforms like AWS SageMaker, Google AI Platform, or even Azure Machine Learning can handle this kind of workload. These services can also help you monitor the performance of your AI in real-time, which is critical when it comes to live trading.
Visualizing Trading Data: Since you’re using visual processing for trading, you’re on an interesting path. There are tools like TensorFlow and OpenCV (computer vision libraries) that can help you with visual recognition of chart patterns. In terms of already-trained models, there isn’t much pre-built for visual trading specifically, but you can certainly leverage general-purpose object detection models or even build custom ones that can identify patterns on charts (like price action movements or specific chart formations). For real-time visual data, using something like a custom image recognition model on live trading charts could be a game-changer, especially if you want to detect certain patterns as you do.
Cloud vs. DIY: Regarding whether you should use platforms like AWS, IBM, or Google Cloud: if you’re just starting and trying to get everything set up, using a cloud service will save you time and provide scalability. They offer managed solutions for deployment, monitoring, and updating, which is helpful when you’re focused on building the actual trading logic. You could also consider using a hybrid model, where you prototype and test locally (on your own system) and then scale up to the cloud when you’re ready to deploy at full capacity.
Collaboration with Data Scientists: If you’re looking for regular conversations with data scientists, forums like Stack Overflow, Reddit’s /r/MachineLearning, or even specialized communities on GitHub could be great places to connect. There are also a lot of data science groups that focus on financial modeling and AI in trading, so you might be able to find people working on similar problems who can help you out.
Overall, it sounds like you’re on a fascinating journey, and while it might feel overwhelming at times, don’t be afraid to reach out for help when needed. You’ve already got a lot of the core concepts down, and with the right tools and support, you can definitely get to where you want to go!
