AI/ML Prediction Market Developer

@Fazeennazar

Hello, The real challenge here is not generating predictions, but proving that signals remain valid on unseen data without look-ahead bias, data leakage, overfitting, or unrealistic backtest assumptions. I’d build separate pipelines for real-time Kalshi/available Robinhood data, historical storage, live news/events, feature engineering, strategy models, confirmation, paper trading, and performance tracking. For 15-minute markets, I’d test momentum, order-book/liquidity changes, probability movement, mean reversion, volatility, time-to-expiry behavior, and event/news signals. Each strategy would be evaluated independently before being combined into a confirmation layer; conflicting or insufficient evidence would return NO TRADE. For ML, I’d start with interpretable statistical/gradient-boosting models and use chronological splits, walk-forward testing, timestamped features, out-of-sample validation, and live paper trading to prevent leakage. Every prediction would record the market snapshot, features, model version, probability, supporting signals, news considered, decision reason, outcome, and simulated P/L, allowing performance to be audited by strategy and market condition. I can deliver the real-time pipeline, backtesting/paper-trading framework, AI/news layer, dashboard, monitoring, source code, deployment, and documentation. Best regards

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Real-Time Prediction Market AI for Kalshi & Robinhood ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an experienced AI/ML developer. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for AI-driven trading systems. I will create a robust AI that continuously monitors live markets, analyzes historical and real-time data, and applies various strategies to provide reliable trade signals. ➡️ Why Me? I can easily build your real-time prediction market AI as I have 5 years of experience in AI/ML development, specializing in financial data analysis, algorithmic trading, and rigorous backtesting. My expertise includes data processing, predictive modeling, and API integration. Additionally, I have a strong grip on machine learning techniques, ensuring your system is efficient and reliable. ➡️ Let's have a quick chat to discuss your project in detail and allow me to show you samples of my previous work. Looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ AI Development ✅ Machine Learning ✅ Predictive Modeling ✅ Financial Data Analysis ✅ Algorithmic Trading ✅ API Integration ✅ Data Processing ✅ Backtesting ✅ Statistical Analysis ✅ Real-Time Data Monitoring ✅ Performance Tracking ✅ Reporting Dashboards Waiting for your response! Best Regards, Zohaib

@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

@adeelpirzada

Hi, I am Adeel. I built Boostifai, a multi-tenant SaaS that runs real time data pipelines, applies AI models, and produces confidence scored outputs rather than raw scores, which is the same structure this needs for trade confirmation. For a system like this I would start with the historical data pipeline and backtesting framework, keeping training and test data strictly separated, then layer in live market monitoring and news scanning once the core signal logic is validated on paper trading. Given the scope, I would propose phase one as the data pipeline plus backtesting and paper trading engine for Kalshi first, with Robinhood and live news scanning as phase two. Which market, Kalshi or Robinhood, do you want the first working version built against?

@sufyanasmat38

We have experience building AI/ML systems, real-time APIs, data pipelines, and analytics platforms, including prediction and automation workflows. For this project, we’d propose a Python-based architecture with FastAPI, PostgreSQL/TimescaleDB, WebSockets, ML models, and a modular backtesting/paper-trading engine, with strict time-based validation to prevent leakage and overfitting. We can integrate permitted market/news data, log every signal and decision, implement a selective confirmation/NO TRADE mechanism, and provide a performance dashboard with backtesting, walk-forward, out-of-sample, and paper-trading results. We’ll be transparent about API/data limitations and will not promise a guaranteed win rate. Full source code, documentation, and deployment instructions can be included. We’re ready to review the target contracts and data access and provide a detailed milestone plan, timeline, and fixed estimate.

@ShaikhAneesa

Hi there, I understand the real challenge is building a continuously running prediction system that combines live market microstructure, historical patterns, external events, and independently validated strategies, while confidently returning NO TRADE when evidence conflicts. I’m confident I can build this as a production-grade Python/ML pipeline with strict research-to-live separation. My approach will be to first build the real-time ingestion layer for available Kalshi/approved market data, historical data, and timestamped news/events. Then I’ll engineer features around probability movement, liquidity, spread, volume, time-to-expiration, market structure, and historical analogues. Next, I’ll evaluate statistical and ML models using time-based splits, walk-forward validation, out-of-sample testing, and leakage/overfitting controls. Finally, I’ll implement a measurable confirmation layer where validated signals combine into TRADE CONFIRMED or NO TRADE decisions, followed by live paper trading. Every prediction will be timestamped with market state, features, news context, model output, rationale, entry assumptions, and final result, with performance tracked through a dashboard. Do you already have historical Kalshi/order-book data, or should acquisition be included? Let’s establish data availability first, then lock the architecture, timeline, and fixed cost. Warm Regards, Aneesa.

@Hubready

Hello!, I am a Florida-based senior software engineer and this is the kind of project where precision really matters. A real-time prediction market is not just about training a model, it needs clean data, strong feature engineering, reliable APIs, and a system that stays accurate as conditions change. My approach would be: 1. Define the target, market logic, and success metrics 2. Build the data pipeline for ingestion, cleaning, and storage 3. Train, test, and backtest models with proper statistical checks 4. Expose predictions through a secure, fast API 5. Add monitoring so performance and drift can be tracked over time I’ve built trading and data-driven systems before, so I know the common failure points: noisy inputs, weak validation, latency, and model drift. I focus on solving those early so the final product is actually useful, not just a demo. Relevant work: - A crypto signal engine with live scoring - A sports forecasting dashboard with API output - A stock screening tool with automated data collection - A financial analytics backend with Python and PostgreSQL Quick questions: 1. Are you predicting event outcomes, price movement, or probabilities over time? 2. What data sources are available now, and do you need API integration built from scratch? 3. Do you want an MVP first, or a full production-ready build with backtesting and monitoring? If you want someone who will treat the details seriously and build this the right way, I’d be glad to help. -James

@PINESCRIPTERR

Hi, I can build this as a real-time prediction-market research and paper-trading platform with a Python-based data/ML pipeline, historical database, live market and news ingestion, feature engineering, model evaluation, structured confirmation logic and a performance dashboard. I’d keep the system selective, with NO TRADE when evidence conflicts, and make every prediction reproducible by storing the market state, features, news inputs, model output and decision at signal time. Backtesting, strict time-based validation, walk-forward testing and live paper trading would be essential before evaluating real-money performance. Kalshi/Robinhood integrations would be implemented only through their permitted APIs/data access. Questione 1: Do you already have authorized Kalshi and Robinhood API access, and which specific 15-minute market categories should be prioritized for the MVP? Questione 2: Do you have historical market/order-book data available, or should the first phase include building the data collection pipeline before model development? Regards, Houssame.

@Hrolgar

How many independent signals do you want stacked before a trade actually fires, and does the confirmation engine stay pure rule-based at first or are you expecting ML weighted into the vote from day one? Kalshi's 15-minute markets move fast enough that the ingestion layer has to be solid before anything downstream matters, so phase one is a live Kalshi pipeline, a news scanner tagging relevance to open markets, a rule-based multi-signal engine that only fires when independent strategies agree, full logging, and live paper trading. Nothing unproven in that phase, all of it testable the day it ships. Backtesting, the ML layer, and Robinhood feasibility get scoped and priced once the pipeline's proven and I've seen what the historical data looks like. M1: Kalshi live ingestion, logging, rule-based multi-signal engine, paper trading, $250, 6 days. M2: news relevance scanner wired into the signal vote, dashboard for live paper performance, $275, 5 days. M3: backtesting harness against logged data plus a scoped writeup for ML and Robinhood feasibility, $225, 3 days. The 750 is priced off what's in the brief. Robinhood's API access and rate limits are the piece that could move it once I know whether that's a hard requirement for M1 or a phase-two item. Is Robinhood in scope for the first release or can it wait, what counts as agreement between strategies, does the news scan need a fixed source list, and is there a target number of concurrent Kalshi markets to track?

@ImranAli01

Hello, I went through your project description and understand you need a rigorously tested AI prediction system for Kalshi and supported Robinhood markets, not a basic indicator or unverified signal generator. I’ll focus on measurable performance, reliable data pipelines, and transparent decision logging rather than unrealistic win-rate claims. I’ll build the architecture using Python, FastAPI, PostgreSQL, Pandas, and Scikit-learn, with modular market-data ingestion, historical storage, news analysis, and multi-strategy confirmation. Backtesting will include strict train/test separation, walk-forward validation, out-of-sample testing, and live paper trading to reduce leakage and overfitting risks. Every signal, feature, news input, decision, and simulated result will be logged for auditability. I’ll also implement API retry handling, monitoring, failure recovery, and a performance dashboard covering accuracy, expected value, drawdown, and strategy-level results. Kalshi and Robinhood integrations will first be assessed against their available API access and contractual limitations. Could you confirm which markets and historical datasets you already have access to? Best Regards, Imran

@dvcontact

Hello Sir, Imagine having a sophisticated AI that adapts to market changes in real-time and delivers verified trade signals before any commitment—I'm ready to build a demo for you right now. My extensive experience in developing AI/ML trading systems equips me to create the precise prediction market AI that you envision for Kalshi and Robinhood, ensuring rigorous analysis of live and historical data. Let's discuss how we can implement this cutting-edge solution tailored to your needs, and I can present a detailed plan along with a demo to showcase its capabilities. Regards, Smith

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@jonasdedzo

Hello, I understand you need a serious, evidence-driven AI/ML prediction system for Kalshi and supported Robinhood 15-minute markets—not a basic indicator or prediction script. I can design a modular Python architecture covering real-time market ingestion, historical storage, news analysis, feature engineering, statistical/ML models, confirmation logic, paper trading, and performance analytics. The system will continuously evaluate price, liquidity, order-book behavior, momentum, time-to-expiration, historical patterns, and relevant news before returning TRADE CONFIRMED or NO TRADE. Every decision will be logged with inputs, reasoning, confidence, strategy contributions, and final outcome. I’ll prioritize strict train/test separation, walk-forward validation, out-of-sample testing, leakage prevention, realistic backtesting, and live paper trading before any real-money use. Reliability features will include caching, rate-limit handling, reconnection logic, deduplication, monitoring, and detailed audit logs. Fixed Price: $320| Delivery: 5 Days Deliverables include source code, documented architecture, backtesting framework, paper-trading mode, performance dashboard foundation, and deployment instructions. I’ll clearly identify API limitations, data gaps, and risks rather than promise unrealistic win rates. Thanks!

@codefusion53

Hi I can build this as a quantitative research and real-time decision platform, not a black-box AI that claims an unrealistic win rate. The architecture would separate market/news ingestion, historical storage, feature engineering, model/strategy evaluation, signal confirmation, paper trading, and performance analytics. I recommend Python, FastAPI, PostgreSQL/TimescaleDB, Redis and WebSockets, with Kalshi APIs plus permitted Robinhood/data-provider integrations. For modeling, I would benchmark interpretable statistical models and gradient boosting before introducing more complex ML. News can be classified for relevance, event impact and conflicting evidence using an LLM/NLP layer, while deterministic market features remain independently measurable. Testing would use strict time-based train/validation/test splits, point-in-time features, out-of-sample evaluation, walk-forward testing and live paper trading to prevent leakage and expose overfitting. Every decision would store its inputs, model version, news evidence, confidence, rationale and eventual outcome. I suggest a 10 to 14 week phased build. A realistic initial budget is $8,000 to $12,000 depending on data-provider costs and final integrations. Full source, tests, dashboard, deployment and documentation are included. Best, Justin

@mayr81

Your specification calls for a production-minded prediction platform rather than a simple signal script, and I would approach it with transparency and measurable validation at every stage. I can design a modular architecture covering Kalshi and permitted Robinhood connectivity, normalized real-time market streams, historical storage, news ingestion, feature engineering, multi-strategy confirmation, and selective TRADE CONFIRMED/NO TRADE decisions. Every signal would be recorded with inputs, model versions, feature values, news context, rationale, confidence, invalidation conditions, and eventual outcome. The validation framework would separate training, validation, out-of-sample, walk-forward, and live paper-trading periods to reduce leakage and overfitting. I would benchmark calibrated statistical and ML models against market-implied probabilities, evaluate performance by regime and strategy, and report accuracy, calibration, expected value, drawdown, false positives, and simulated P/L through a dashboard. Reliability would include reconnect logic, deduplication, rate-limit handling, stale-data safeguards, API failure fallbacks, structured logs, restart recovery, and deployment documentation. I would also clearly document exchange/API limitations and avoid unsupported claims about profitability. The delivery can include complete source code, tests, configuration, database schemas, backtesting tools, paper-trading mode, monitoring, dashboard reporting, and deployment inst

@aftabyounas

With my extensive web development background in Laravel, WordPress, React, and Flutter, I am well-equipped to create a sophisticated real-time prediction market AI that aligns with your expectations. Having worked on numerous websites and mobile apps employing CodeIgniter, Symfony, AngularJS, Firebase, MongoDB, and MySQL, I have acquired the ability to adapt to different technologies in no time. Based on my experiences, I am confident that I can build the required AI with all the functionalities you desire.

@tobiask5

Hello, there. I can build the real-time prediction system with Python and ML. I’ll create live market data pipelines, historical storage, and reliable feature processing. I’ll separate training and testing data to prevent leakage and overfitting. I can implement walk-forward testing, backtesting, and live paper-trading evaluation. I’ll combine statistical models, market signals, news impact, and time-to-expiry factors. Every signal will be logged with evidence, confidence, outcome, and simulated P/L. I’ll deliver the source code, dashboard, deployment setup, and clear documentation. Best Regards, Tobias

@p4logics

Dear Sir, I'm an expert in web design and development using Html, CSS, JavaScript, Angular, React js, JSP, Servlet, Java, Spring boot, Spring securities, Webflux, MySQL, MongoDB, Jira, bitbucket, GitHub, aws, docker, Jenkins, etc. My Technical skills are as follows:- Frontend: HTML, CSS, JavaScript, Angular, React js Backend: Node js, Core Java, J2EE (Advance java), Spring boot (Framework) Database: Mysql and MongoDB Web Application: Frontend: HTML, CSS, JavaScript, Angular, React js | Backend: Java (J2EE, Spring boot), Node js Mobile Application: Android (using Java), React Native (Android and IOS) Desktop Applications: Java (JavaFX, AWT, and SWING) Please ping me so we can discuss your project in more detail and can start immediately. Looking forward! Thanks & Regards

@vishal1145

Your system will fail if the news sentiment layer cannot distinguish between noise and actual market-moving events in under 30 seconds. Most prediction market AIs I've reviewed collapse because they either overreact to irrelevant headlines or miss critical signals during the final 5 minutes before expiration when liquidity evaporates. Quick questions - are you planning to run this on dedicated infrastructure with sub-100ms API latency to both Kalshi and Robinhood? And what's your acceptable false-positive rate when the model says NO TRADE versus forcing a position? Here's the architectural approach: - PYTHON: Build a multi-threaded event-driven pipeline using asyncio to ingest live order book data, news feeds, and historical patterns simultaneously without blocking execution. - MACHINE LEARNING: Train ensemble models (XGBoost + LSTM) on walk-forward validation splits with strict out-of-sample testing to prevent overfitting, then deploy a meta-classifier that weighs confidence across momentum, volume, and sentiment signals. - API DEVELOPMENT: Design a low-latency REST + WebSocket hybrid architecture to stream real-time Kalshi market data and integrate Robinhood's unofficial API with rate-limit handling and automatic failover during outages. I've built similar real-time trading systems for 2 fintech clients processing 500+ signals per second with 200ms decision latency. Let's schedule a 20-minute technical call to validate your data sources and define the backtesting framework before we architect the confirmation logic.