Self-Learning Polymarket Trading Bot

@AwaisChaudhry

Hello, I’m Muhammad Awais. I will build a modular, ML-driven trading bot for Polymarket with a simple Windows CLI. It will collect real-time odds, track recent price moves, and learn from past trades to update its strategy without hard-coded rules. The design focuses on clear, well-documented Python code for data collection, a swappable model layer, and a trading engine that uses the latest model. It will feature position sizing limits, error handling for API downtime, and a dry-run mode for safe testing from day one. The project will be split into: data collection scripts, a model-training and inference module, and a trading interface that executes orders via Polymarket once the model is ready. I will include a README with setup, config options, and steps to retrain as new data comes in, plus a short demo video or walkthrough showing trades on a test account. What is your preferred model type to start with for the initial 15-minute decision loop, and how do you want to handle data versioning for retraining? What are the exact API endpoints for market data and order placement, and do you require multi-account safety checks in dry-run mode? What is the maximum drawdown you’ll accept in backtests and live runs? Should the system support alternating bet sizes or fixed units? How will you supply historical data for initial training, and what features are most important to you (odds spread, volume, time-based signals)? Do you want built-in backtesting with walk-forward validation,

@leadconcept

Greetings, We understand you are looking for a Python-based trading bot for Polymarket that automatically takes Low/High BTC positions every 15 minutes, uses machine learning to refine its strategy over time, and includes modular retraining, logging, and safety features like position limits and dry-run mode. Before proceeding, we would like to clarify a few points: 1, Do you have access to Polymarket’s API or should we handle data scraping and API integration 2, Should the ML model be designed for real-time inference only, or also support batch backtesting on historical data 3, Are there any specific risk management rules or position sizing preferences to incorporate from day one Our team includes Python developers and ML engineers experienced in algorithmic trading, reinforcement learning, and predictive decision systems. We focus on modular architecture, reliable API integration, clear documentation, and safe, reproducible trading with full logging and retraining support. Let us connect to discuss model design, trading logic, and development timeline. The current bid amount is a placeholder to start the conversation. Regards Yasir LEADconcept PS: Let me know, if you want to see our team past work to determine our skills/expertise or past customer's references.

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Trading Bot for BTC on Polymarket with Machine Learning ❇️ Hi My Friend, I hope you are doing well. I reviewed your project needs and see you are looking for a trading bot for Polymarket. You don’t need to look any further; Zohaib is here to help you! My team has completed over 50 similar projects for trading bots. I will create a bot that automatically takes “Low/High BTC” positions every 15 minutes, analyzes outcomes, and improves its strategy as more data comes in. ➡️ Why Me? I can easily build your trading bot as I have 5 years of experience in Python programming, machine learning, and automation. My expertise includes developing trading algorithms, data analysis, and creating user-friendly interfaces. Additionally, I have a strong grip on real-time data processing and API integration, ensuring a robust solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail and I can show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python Programming ✅ Machine Learning ✅ Trading Algorithms ✅ Data Analysis ✅ API Integration ✅ Error Handling ✅ CLI Development ✅ Back-testing Strategies ✅ Real-time Data Processing ✅ Logging and Monitoring ✅ Modular Architecture ✅ Documentation Writing Waiting for your response! Best Regards, Zohaib

@OutsourceMan

⭐⭐⭐⭐⭐ Dear Valuable Client, CnELIndia, led by Raman Ladhani, can successfully deliver your BTC trading bot by leveraging our extensive experience in Python-based algorithmic trading and machine-learning systems. We propose building a modular architecture with separate data-collection, model-training, and trading-engine components, ensuring easy retraining and strategy refinement. Our ML models will combine real-time market odds, price movements, and historical performance to drive automated Low/High BTC positions every 15 minutes. Safety features—including position limits, API error handling, and dry-run/backtesting—will be implemented from day one. We will provide well-documented Python code, a comprehensive README for setup and retraining, and a short demo showing successful trades on a test account. Raman Ladhani’s expertise in AI model development and ML-driven trading ensures high reliability, maintainability, and performance. We look forward to making this project a success.

@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 Matlab, Python, and similar tools. 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

@efanntyo

Hi there, ⭐ I’m Efanntyo, a seasoned full-stack developer and AI specialist with extensive experience in building AI-driven decision systems, trading bots, and scalable architectures. For your Self-Learning Polymarket Trading Bot, I will deliver a modular Python-based solution with a clear CLI, robust logging, and a retrainable ML core that uses real-time odds, recent price movement, and historical trade performance to drive decisions. The architecture will separate data collection, model training, and the trading engine so retraining is painless and safe. What I’ll deliver: - Well-documented Python code (data collection, model training, prediction, order execution) and a dry-run/back-test mode from day one. - A configurable pipeline: plug in new models, swap retraining datasets, and trigger retraining with minimal downtime. - Safety first: position sizing limits, API downtime handling, error recovery, and a clear logging trail. - A README with setup steps, configuration options, and examples to retrain as new data accumulates. - A short demo video or live walkthrough showing test trades on a sandbox account. My approach: 1) Build a lightweight CLI and robust logging; 2) Implement modular data ingestion and a model abstraction layer; 3) Integrate a trading engine that calls the latest model; 4) Add back-testing, dry-run, and safeguards; 5) Demonstrate with a small, staged proof on a test account. What are your preferred data sources for real-time market odds and which Pol

@sdqtrades

Being a seasoned Python developer, I come equipped with an impressive background in building trading bots and ML-driven systems that cater to unique user needs just like yours. With practical experience in AI development, algorithm design, and software architecture, I will create a self-learning polymarket trading bot that embodies your vision exactly. I intend to harness the power of machine learning algorithms, combining real-time market odds, recent price movement, and historical trade performance for informed decisions every 15 minutes. The entire process will be driven by data collected by modular scripts which you can effortlessly retrain or swap out models when needed. Recognizing the need to ensure safety and maintain long-term maintainability, my approach includes incorporating features such as position sizing limits, error handling for API downtime, as well as dry-run/back-test switches from day one. Rest assured, each line of code will be well-documented and accompanied by detailed instructions so you are fully empowered to manage your bot autonomously when required.

@Feriver

Hello, I can build a self-learning Polymarket trading bot in Python that automatically takes “Low/High BTC” positions every 15 minutes, evaluates outcomes, and refines its strategy using machine learning. The architecture will separate data collection, model training, and the trading engine, allowing seamless retraining as new data arrives. The bot will incorporate real-time market odds, recent price movements, and historical trade performance, with modular ML models that can be swapped or updated. Safety features include configurable position sizing, API error handling, and a dry-run/back-test mode. Deliverables include fully documented Python source code, setup/configuration instructions, and a demo showing successful test trades. Clarification Questions: Do you want the bot to maintain state across restarts, or reset learning each session? Should the ML model be retrained continuously in real time or periodically on accumulated batch data? Thanks, Asif.

@mobimubasir

I have actively engaged in automating intricate processes and making them efficient - a valuable trait when it comes to your need for safety features like position sizing limits, error handling for API downtime, and dry-run/back-test switch. Crucially, my past projects have involved building algorithmic models involving real-time market odds and historical trade performance. This not only familiarized me with the niche trading domain but also taught me how best to approach combining multiple data streams so as to enhance model accuracy.

@bindmission

Hello, I have 10 years of experience in developing AI-driven trading systems and automated decision-making bots. I am confident in delivering a Self-Learning Polymarket Trading Bot tailored to your needs. I will implement the core logic in Python with optional frameworks to enhance performance. The bot will feature modular design, safety measures, and simple CLI operation on Windows. I possess extensive experience in trading bots and machine learning systems. Regards, VishnuLal NB*

@juanjosec63

Hello, I’m excited about the opportunity to contribute to your project. With my expertise in Python trading bots, Polymarket market-data ingestion, ML-based signal modeling (real-time odds + BTC price features + historical PnL), modular retraining pipelines, and a Windows CLI execution layer with robust logging, I can deliver a solution that aligns perfectly with your goals. I’ll tailor the work to your exact requirements, ensuring smooth integration, reliable performance, safety controls (position sizing limits, API downtime handling), and clean switches for dry-run/backtesting vs live execution while keeping the model layer easily swappable and retrainable. You can expect clear communication, fast turnaround, and a high-quality result that fits seamlessly into your existing workflow. Best regards, Juan

@HiraMahmood4072

Hello, With over 7 years of experience in Algorithm and Python development, I have carefully reviewed your requirements for the self-learning Polymarket trading bot. I am well-equipped to handle the project efficiently. To achieve the desired outcome, I propose developing a Python-based trading bot that will autonomously analyze market trends every 15 minutes, utilizing machine-learning algorithms for decision-making. The bot will continuously refine its strategy based on real-time market odds, recent price movements, and historical trade performance. The architecture will feature modular data-collection scripts, a flexible model layer for easy retraining, and robust safety features like position sizing limits and error handling for API downtime. The deliverables will include well-documented Python source code, a comprehensive README, and a demonstration of successful trades on a test account. I would like to discuss the project further with you. Please connect with me for a detailed conversation. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@trm66614

Hi there,Good evening I am Talha. I have read you project details i saw you need help with Algorithm, AI Development, AI Research, Data Analysis, Python, AI Model Development, C++ Programming, C Programming, Artificial Intelligence and Software Architecture I am writing to propose an innovative approach to tackle your project. Our proposal centers on delivering creative and effective solutions that will set your project apart. We will present fresh, out-of-the-box ideas that align with your project's objectives, demonstrating how we can achieve remarkable results. Please note that the initial bid is an estimate, and the final quote will be provided after a thorough discussion of the project requirements or upon reviewing any detailed documentation you can share. Could you please share any available detailed documentation? I'm also open to further discussions to explore specific aspects of the project. Thanks Regards. Talha Ramzan

@NeuralNetWorker

Hola, soy Javier. Cuento con amplia experiencia en Data Analysis y Visualización. Me interesa el proyecto, pero para garantizar un dashboard útil y sin errores de base, es imprescindible aclarar estos puntos críticos y definir bien el alcance: Origen y Formato: ¿Listados en papel, Excel manual, CSV o conexión directa a ERP/CRM? Integridad: ¿Los datos están limpios o requieren transformación previa (ETL)? Lógica de KPIs: ¿Las fórmulas de "productividad" y "margen" ya están definidas o hay que crearlas? Actualización: ¿Carga de datos manual o automatización en tiempo real? Si te parece bien, podemos comentar estos detalles técnicos para cerrar la propuesta. Un saludo Javier Montero

@DigitaSyndicate

You are not looking for a coder. You are looking for someone who can build this properly. That is exactly why your project stood out. Your ambition to craft a modular, Python-based trading bot leveraging machine-learning for adaptive decision-making on Polymarket reflects a commitment to precision and continuous optimization that aligns perfectly with how we architect solutions. At DigitaSyndicate, a UK-based digital systems agency, we build precision-engineered automation and AI-driven systems designed for long-term scalability and reliability. Our approach emphasizes modularity and robust error handling, ensuring seamless model retraining, safe operation with position sizing limits, and transparent logging—all critical for your desktop CLI environment. Having delivered analogous ML trading engines that integrate real-time data streams with adaptive models, we bring both the technical and strategic insight necessary here. Can you share your main priorities and timeline so I can map out the right execution plan for you? Casper M. Project Lead | DigitaSyndicate Precision-Built Digital Systems.

@muneeb6767

I can build your ML-driven Polymarket BTC High/Low bot with modular data collection, retrainable model layer, risk controls, backtesting mode, and clean Windows CLI logging — I’ve developed automated trading & ML decision systems with live API integrations before. You’ll get documented Python code, structured architecture, and a working demo on test trades.

@hayat38402

Hi, how are you doing? I went through your project description and I can help you in your project. your project requirements perfectly match my expertise. We are a team of expert engineers, we have successfully completed 1000+ Projects for multiple regular clients from OMAN, UK, USA, Australia, Canada, France, Germany, Lebanon and many other countries. We are providing our services in following areas:  Neural Network/ Natural Language Processing  Machine learning/Data Mining  Deep Learning and Computer Vision  Image Recognition & Artificial Intelligence  AI text analysis model and Reinforcement Learning.  Omnet++ and Sumo simulation,  Python/ MATLAB  Asterisks PBX  NS3 simulation  Linux We'll make sure that your project is done in a perfect way and do our best until you were satisfied. I am confident I can provide you with top-notch materials that will fit your needs.

@vishal1145

Your biggest risk here isn't the ML model - it's building a system that loses money faster than it learns. Polymarket's 15-minute BTC markets have thin liquidity and wide spreads that will eat your bankroll before any algorithm finds an edge. Before architecting this, I need clarity on two things: What's your acceptable drawdown threshold? If the bot loses 20% in the first 100 trades while gathering training data, does the project continue or stop? What data sources are you planning to feed the model beyond Polymarket odds? Real-time BTC price feeds, order book depth, Twitter sentiment - the model's predictive power depends entirely on signal quality, not algorithm complexity. Here's the architectural approach: PYTHON + SCIKIT-LEARN: Build a modular pipeline where feature engineering, model training, and inference are separate scripts. This lets you swap XGBoost for neural nets without rewriting the trading engine. POLYMARKET API + RATE LIMITING: Implement exponential backoff and circuit breakers so API failures don't cascade into missed trades or duplicate orders during network hiccups. POSTGRESQL + TIMESCALEDB: Store every trade, prediction, and market snapshot with microsecond timestamps. This historical data becomes your retraining dataset and lets you backtest strategy changes without risking capital. POSITION SIZING LOGIC: Use Kelly Criterion or fixed-fractional sizing based on model confidence scores. A 55% win-rate prediction shouldn't bet the same amount as an 80% prediction. DRY-RUN MODE: Every execution path must support paper trading with realistic slippage simulation. I've seen too many bots that work in backtest but fail live because they assume instant fills at mid-price. I've built 4 algorithmic trading systems over 8 years, including a crypto arbitrage bot that processed 2M trades across 6 exchanges and a sports betting ML model that achieved 12% ROI over 18 months. I don't take on projects where the client hasn't thought through risk management. Let's schedule a 20-minute call to discuss your data pipeline and capital allocation strategy before I commit to a build timeline.

@mayankinnovative

Hi, As per my understanding: You need a Windows-based Python trading bot that automatically takes “Low/High BTC” positions on Polymarket every 15 minutes using ML-driven decisions—not rule-based logic. The system must collect live odds and market data, learn from historical performance, retrain easily, and include risk controls (position sizing, API failure handling, dry-run/backtest mode). Modular architecture and clear CLI logging are required. Implementation approach: I will design a modular pipeline: Data Layer – real-time Polymarket odds ingestion + historical trade storage (SQLite/PostgreSQL). Feature Engine – rolling price movement, volatility, implied probability shifts, outcome history. Model Layer – pluggable ML models (e.g., XGBoost/LSTM/ensemble) with retrain script. Trading Engine – prediction call → risk filter → position sizing → order execution via API. Safety Controls – max exposure limits, cooldown logic, retry/backoff for API errors, dry-run/backtest switch. CLI logging + structured logs for auditability. Full documentation and retraining guide included. A few quick questions: Do you already have Polymarket API access? Capital allocation per trade? Preferred ML framework (scikit, PyTorch, etc.)? Timeline for first working version?