AI Market Trend Predictor

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@EngrAnasAwan5

AI-powered market prediction systems are core to my expertise, and as a certified IBM AI and Machine Learning Engineer, I bring a wealth of experience that focuses on LLM-powered applications and scalable machine learning solutions designed to deliver measurable business impact. Over the course of my career, I have successfully delivered over 180 AI/ML projects while ensuring a 100% on-time & on-budget delivery rate, something extremely crucial for any time-sensitive project like yours. What sets me apart is not just my technical prowess in utilizing TensorFlow or PyTorch (or whichever framework we agree upon) but also my ability to leverage my expertise in automation & backend engineering. This allows me to build end-to-end AI pipelines from data to model to production; an invaluable asset when it comes to daily refreshed dataset like yours. My not just build models - I build AI systems that work in production. Seen from being one of the top 3% preferred freelancers, this statement resonates well with my clients. So while delivering the high-performance solution you seek, you'd also be getting clean, maintainable, production-ready code with long-term support and scalable architecture Combining all these factors: LLM-powered prediction aptitude, a deep understanding of root causes behind market trends and the ability of converting data into usable features while steering away from historical/social signals come together to make me the best fit for your project.

@sdqtrades

Given your project's need for an AI model to predict market trends based on real-time feed, my extensive experience in Machine Learning and Software Architecture makes me an excellent fit for your needs. I'm well-versed in Python, your language preference, and have a strong understanding of the APIs and cloud hosting solutions like AWS. I've worked with TensorFlow and PyTorch, but if you're looking for a lighter solution, I can leverage my skills with other ML libraries for scheduled retraining purposes. In addition to my ML proficiency, I bring with me a rich background in complex web scraping and smart data extraction—a vital skillset for transforming the real-time market data into usable features for your prediction engine. I believe in delivering fast and quality results while keeping thorough communication—ensuring a precise alignment with your needs, running through a quick cycle from data pull to prediction generation to showcase. Your project will be treated with the utmost confidentiality, as is implied by NDA signed; you retain full ownership of all Intellectual Property generated while working together. You can also count on well-optimized, understandable code that's delivered promptly without compromising quality standards. Let's harness the power of AI to steer your investments towards profitable outcomes!

@vashishtrupinder

I'll write a Python fetcher to pull the current price data, compute intraday aggregations, rolling averages, and volatility indicators, then store the raw and derived values. Each 24-hour cycle the model will refresh those features and retrain an XGBoost classifier to output JSON with direction, confidence, and the key indicators behind each call. I'll containerize the pipeline, document setup, environment variables, and cron trigger in the README, then run one full cycle from data pull to prediction as a demo. I can start right away.

@mahads31

I'm Mahad Sheikh, a highly skilled AI and Web Developer with a wealth of experience across multiple industries. From the outset, I want to assure you that my specialization is in turning complex demands into efficient, reliable solutions - exactly what your AI Market Trend Predictor project requires. Drawing on my expertise in AI Model Development and Web Development, I will build an AI model that ingests only today’s live price data and turns it into concise JSON or CSV reports with price direction, key indicators, and confidence scores - exactly in line with your requirements. When it comes to the framework for this project, 지Etsy. I am comfortable using Tensorflow or PyTorch; however, if you're looking for a lighter yet equally capable ML library that may reduce turnaround time without compromising on quality, I can surely explore those options too. Moreover, as you mentioned your preference for a lightweight cloud instance like AWS for hosting the solution, I want to highlight that Cloud Infrastructure & Deployment is another skill set of mine. Your project calls for not just developing but also deploying the solution and maintaining its schedules- all of which I have considerable experience doing. My aim is always to solve real-world problems efficiently and foster long-term client relationships through reliable project delivery.

@ammarahmed20

As an experienced professional in both AI Workflow Automation and Web Development, I am confident that I can provide precisely what you need for your project. I've spent 6+ years creating efficient, fully-operational technologies that facilitate seamless automation and are built for actual deployment. Your project aligns perfectly with my skill set and experience. I've worked extensively with Python, which is a language you're comfortable with and proposed to use for this project. Furthermore, I'm well-versed in frameworks like TensorFlow, PyTorch, and PyTorchLightning, as well as other lighter ML libraries that support retraining. These tools will help me develop the AI model you seek—an engine that ingests real-time supply data from Tree Market, processes it efficiently, and produces accurate predictions confidently. Another aspect on which I consistently deliver is scalability of projects. You mentioned using a lightweight cloud instance, like AWS; however, given your comfort level with alternative solutions, I'm also open to suggesting others that can reduce the turnaround time even further. With these skills, experience and an emphasis on clear communication paired with fast delivery, I vow to not just meet your expectations but exceed them. Choose me to build this crucial system because your project deserves nothing short of true expertise!

@Shrutibimpl

Hello, I can build and deploy your automated AI market prediction engine in Python on AWS. My plan is to write a data ingestion script using Python and Pandas to fetch live price feeds from the Tree Market API and construct automated technical features. I can build a machine learning model using PyTorch or Scikit learn to calculate price direction probabilities and confidence scores. I can set up an automated daily scheduler using cron or AWS EventBridge on an EC2 instance to execute data updates retrain model parameters and generate output reports in JSON or CSV format. In a past project I built an automated Python time series prediction engine on AWS that ingested live market data and exported daily JSON forecast reports. 1) Does the Tree Market price feed provide a REST API or WebSocket connection for live data ingestion? 2) Do you prefer a Scikit learn model like XGBoost or a deep learning framework like PyTorch for the prediction engine? 3) Which cloud hosting environment such as AWS EC2 or DigitalOcean do you plan to run the daily scheduler on? Thanks, Bharat

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe)& I can build a lightweight daily price-forecasting engine that ingests live Tree Market prices,creates usable features,generates daily trend predictions,& exports JSON/CSV reports automatically My approach: -Review the live price feed format,available intraday fields,market hours,missing ticks,& target definition:next-day direction,price change,or trend class -Build a Python pipeline for daily ingestion,validation,feature creation,prediction,& report export -Since only today’s data is available,start with robust short-horizon features:open/high/low/latest price,volatility,intraday momentum,spread -Use simple reliable models first: Logistic Regression/Random Forest/XGBoost for direction,with calibration for confidence scores -Schedule daily execution using cron,GitHub Actions,AWS Lambda,or a small EC2 container -Save each run’s raw data,features,prediction,confidence,& logs for auditability Relevant experience: -Built cold-start electricity-load forecasting pipelines where limited current/future signals had to be transformed into reliable daily predictions with feature engineering,validation,automated runs -Developed industrial time-series forecasting workflows using sensor streams,rolling statistics,volatility-style features,trend indicators,anomaly flags,& scheduled model refresh logic -Worked on financial/market analysis pipelines involving price trends,rolling returns,volatility,direction classification,confidence scoring

@shayankhan333

Training a fresh model daily on only one day of live prices will overfit fast, the real trick is a rolling feature window plus recalibration, not full retrain. I'll build a Python fetcher, a LightGBM predictor with confidence scoring, and a cron container on a small EC2 that writes JSON daily. One catch: without any history baseline, "confidence" needs a calibration step or the score is noise. 1) Which Tree Market endpoint or API feeds the live prices? 2) Is a 7-day rolling cache acceptable, or strictly today only? Cheers Shayan

@tyronet2

Your main challenge is not scheduling the pipeline—it’s producing a defensible daily forecast when the model is restricted to today’s price data and has no historical sequence to learn from. I’ve built production Python ML/data workflows at Marin Software using AWS, automated pipelines and model-driven processing, so I’d first design the data ingestion and feature layer to make the strongest use of the live feed, then choose the lightest suitable model rather than forcing TensorFlow or PyTorch where it adds no value. The service can run as a Dockerized Python job on AWS, refresh every 24 hours, generate direction/confidence outputs in JSON or CSV, and retain each run for evaluation. I’d also document scheduling, environment variables and deployment clearly. One important point: if only a single day of prices is available, prediction quality will be inherently limited. Can the system retain its own daily snapshots after launch so it gradually builds a historical dataset?

@Anika1802

Hi, We carefully studied the description of your project and can confirm that we understand your needs and are interested in your project. We have the necessary experience and expertise to start your project as soon as possible. We have strong experience with Python, machine learning, data processing, predictive models, API integrations, scheduled pipelines, AWS deployment, and automated reporting. We can build a lightweight prediction engine that fetches the current market feed, generates features, runs the forecasting model, and produces daily JSON/CSV predictions with direction and confidence scores. We can structure the solution for automated daily execution and retraining/recalibration, with a clean Python codebase, containerization where useful, and clear documentation covering setup, scheduling, environment variables, and deployment. Please contact us via Freelancer Chat to discuss the market data source, available API, prediction requirements, and preferred cloud environment in detail. Best Regards, Vandini

@ssquare489

Your requirement to use only today’s live Tree Market prices means I’d design Phase 1 as a lightweight intraday forecasting pipeline rather than a heavy deep-learning model that pretends one day of data is enough for robust retraining. I’d use Python with pandas, scikit-learn/XGBoost, and a small scheduler such as cron or APScheduler inside Docker. My two priorities would be clean implementation and maintainability: fetch the live feed, validate/normalize it, generate intraday features such as momentum, volatility, range, moving averages, and price change, then output direction, confidence, and derived indicators as JSON/CSV every 24 hours. One important point: with no historical data, the model can still score short-term patterns from the current day, but meaningful supervised training and confidence calibration will be limited. I’d therefore store each daily snapshot from launch onward so the system naturally builds a training history without changing your current data scope. Deployment can run on a small AWS EC2 instance, Lightsail, or Render depending on cost and simplicity. The daily job would log each run, preserve input/output files, and fail visibly if the feed is unavailable. A relevant project is a live stock-market analysis platform I worked on using GDFL WebSocket data for real-time market processing and trading-oriented dashboards. Estimated first working version: 5–7 days.

@iterativeTech

Using my technical skills combined with my business-first mindset, I can create a robust AI prediction engine for your financial needs. I specialize in using Python and working with lightweight cloud instances like AWS, both of which align with your preferences. For your time-sensitive project, I propose Tensorflow as the primary framework due to its broad functionality and support for scheduled retraining—a feature that adds value to your downtime-sensitive venture. My expansive experience spans numerous industries, including finance, healthcare, and insurance—situations that have taught me to work with immense focus on ROI. My high geared AI development skills are demonstrated in the creation of generative AI applications, recommendation systems, classification models et al. This is a comprehensive indicator of how well-equipped I am to develop an exacting model like this. Moreover, my proficiency stretches to creating intuitive READMEs and providing quick run demos. The best indicator of my fit for this task is not only my proficiency, but also how much I believe in maximizing client satisfaction. This wholesome package ensures speedy turnarounds and an increased efficiency characterized by why my clients courteously characterized me in their feedback as an expert who delivers tangible solutions which aligns perfectly with what you're seeking. Let's get started!

@aakaashberi

Your brief says ingest only today's live prices, no history. That is the one line that cannot work as written, and I would rather say it now than bill you for a model that learns nothing. The fix is small: the pipeline keeps every day it ingests. Day one it seeds from whatever price history the feed exposes, then every 24h it appends the new close and recalibrates. Same cadence you asked for, but now there is something to learn from. Second honest point: nobody predicts price direction reliably, and a bidder who promises it is selling you a backtest that will not hold. What I deliver is a calibrated confidence score - when it says 60%, it is right about 60% of the time - plus a walk-forward test on held -out days so you can see the real hit rate before trusting it. Stack: Python with gradient-boosted trees rather than a deep net - on daily bars they beat TensorFlow models and retrain in seconds on a small instance. Containerised, cron-scheduled, JSON and CSV out. One question: what is the Tree Market feed, and does it expose any past closes? That single answer sets how soon the model is useful.

@priyanshuk879

Hi, I can build a lightweight Python prediction engine that pulls the current Tree Market price feed and generates daily forecasts. I’ll structure the data ingestion and feature processing so the model can refresh automatically every 24 hours. For the first version, I’d use a lightweight ML library such as scikit-learn unless the data requires a deeper model. The output will include price direction, confidence score, and useful indicators in JSON or CSV format. I’ll package the solution as a script or Docker container so it is easy to deploy on AWS or another cloud server. I’ll also set up scheduled execution for the daily data refresh and prediction cycle. You’ll receive a clear README covering setup, scheduling, and environment variables. I’ll provide a demo showing the complete flow from live data collection to prediction output. Are you available for a quick chat to discuss the Tree Market data source and expected prediction format? Ready to start Immediately.

@novatech2210

⚠️ IF YOU'RE NOT HAPPY YOU DON'T PAY ⚠️ I think we're a strong fit for your project. I specialize in Java, Software Architecture, Google App Engine, Machine Learning (ML). For this brief (I need an AI model that can look at current market prices in the Tree Market and turn that real-time) I would isolate the bottleneck, confirm acceptance criteria, and ship a clean, measurable fix you can verify in staging before it hits production. I'd keep the architecture simple, secure, responsive, and easy for you to manage after handover. Multiple 4.0-rated reviews on Freelancer (16 total), payment verified. I can start against a 7-day delivery window. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, N0VATECH

@MuhammadAkif8

Having successfully completed over 500 projects and with 10+ years of professional experience under my belt, I am undeniably well-equipped to deliver an AI Market Trend Predictor that aligns with your specific needs. My web development expertise, alongside my knowledge of Machine Learning and AI model development, makes me the perfect candidate for this project. In line with your project requirements, I plan to implement TensorFlow as the framework for this solution due to its ability to support scheduled retraining. As for the platform, we can swiftly deploy your AI model on a lightweight cloud instance such as AWS or any other preferable alternative that ensures fast turnaround time. Regards AKif A

@ibtemirlan

I can deliver a bounded first milestone: a working daily prediction pipeline and baseline model, without claiming reliable forecast accuracy before historical validation is possible. Scope: 1. Connect to the documented Tree Market price API/feed and validate incoming data. 2. Build automatic features from the available intraday observations. 3. Produce direction, confidence and key indicators in JSON/CSV using a lightweight Python model. 4. Package it in Docker and configure a 24-hour scheduled run with stored outputs. 5. Provide a README and demo of one complete cycle. Delivery: 14 hours, typically 3–4 days. Fixed price: ₹30,000. This milestone excludes a production accuracy guarantee, dashboard and extensive cloud infrastructure. Is there a documented API with intraday timestamps? What exact future horizon and target price should “direction” represent?

@bhargav922002

Hello, I’m Bharghav, and I bring over 10 years of experience in matching job skills, particularly in Machine Learning and software development. My expertise aligns well with your project requirements for building an AI market trend predictor. I understand that your primary goal is to develop a robust AI model that processes daily market price data and generates reliable forecasts. I propose to create a prediction engine that ingests today’s live pricing data, automatically transforms it into usable features, and generates daily output in either JSON or CSV format. The model will be designed to refresh its dataset every 24 hours, ensuring that recalibration and retraining occur seamlessly. Based on your preference for a cloud-based solution, I suggest deploying it on AWS, utilizing TensorFlow for its efficient scheduling capabilities. I would love to discuss your specific needs in further detail. Please start a chat so we can explore the best approach together. Best regards, bhargav922002