Polymarket Trading Bot Development

@irsolutions

With over a decade of experience in full-stack architecture and high-scale systems, I understand your need for a Polymarket trading bot that predicts short-term BTC price movements. My background in developing high-security systems and scaling applications for over 1 million users directly applies to the complexity of this project. To ensure scalability and accuracy, I recommend utilizing an LSTM model for ultra-short time series classification. In a similar project, I successfully built and scaled a Telegram Mini App serving over 1 million users, showcasing my ability to handle high-frequency trading environments. I am confident that my experience in Python, real-time WebSocket pipelines, and previous work with crypto trading bots make me a strong fit for this project. I look forward to discussing the roadmap with you further to establish a timeline and deliver a high-performance trading bot for Polymarket.

@r4rony

Hi, I can build your Polymarket BTC scalping bot with real-time data, ML prediction, and automated trading. Experience: Built crypto trading bots using WebSockets + ML signals Developed FastAPI + Streamlit systems for real-time prediction & execution Model: XGBoost (better for 10s prediction) Faster inference (low latency) Handles noisy, short-term data well Uses order book + momentum features effectively Deliverables: Real-time data pipeline (price, order book, Polymarket) ML model for 10s price movement probability FastAPI backend + trading logic Paper + live trading integration Simple dashboard We can communicate more on this matter. Kindly accept the offer so we can proceed. Thank you Jennifer

@OutsourceMan

I want to bring my impeccable track record of over 18 years and the cutting-edge skills of my team at CnELIndia to deliver on the Polymarket Trading Bot project. Our extensive experience in creating complex trading bots across various platforms including cryptocurrency gives us a unique insight into understanding your specific needs. I can develop a bot that predicts price fluctuations at an ultra-short scale, in your case within 10-seconds. In terms of model choice, we're confident in delivering both LSTM and XGBoost models. We believe that for your project LSTM could be ideal, given its proven capability with ultra-short time series classification which suits your prediction requirements perfectly. Moreover, in terms of frontend and backend development, our expertise in technologies like React, Streamlit, and FastAPI will ensure we provide you with not just a functional backend API that can integrate with Polymarket CLOB API but also a user-friendly frontend dashboard where you can track trades in real-time. As for timeline estimations, we take deadlines seriously and have consistently met or beat them for over 743 clients. Once awarded this project, we will come up with a realistic yet aggressive timeline based on the complexity and size of the tasks at hand. With us, you can expect high-quality deliverables on time and on budget. Let's discuss further how we can make this project a resounding success together!

@softsolution2000

I have extensive experience in developing trading bots and machine learning models for crypto markets. I have built similar systems utilizing LSTM for ultra-short time series classification. My past projects include creating trading bots for various cryptocurrencies and implementing real-time WebSocket pipelines. I am confident in my ability to integrate with Polymarket's API and deliver a robust solution within the specified timeline. Please review my profile for a comprehensive overview of my work. Let's discuss the project details further and adjust the budget accordingly. I am eager to showcase my commitment to this project and begin working on it promptly.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re looking to develop a trading bot for Polymarket that will handle the “BTC price up/down in 5 minutes” binary market. It sounds like an exciting challenge! I’ve worked on similar trading bots and understand the key technical challenges involved, especially around real-time data processing and strategy implementation. My approach would involve building a robust backend using Python and FastAPI, integrating with Polymarket's API, and ensuring smooth execution of trades based on your specified parameters. I have a few questions to get a better understanding: Q1 – What specific trading strategies do you want the bot to implement? Q2 – Are there any particular user roles or access levels needed for this bot? Q3 – Do you have existing systems in place that the bot needs to integrate with? Looking forward to hearing from you.

@toni5705

Hi, A Polymarket scalping bot that predicts BTC price direction in 10 seconds, executes "Yes" token purchases on uptick signals, and exits immediately after a small profit — real-time data pipeline, ML model, FastAPI backend, and live trading integration. This sits squarely in our ML engineering and crypto trading experience. What we deliver: - Real-time data pipeline ingesting BTC spot price (1s), VIX, Polymarket token price, and order book data via WebSocket - ML model trained on ultra-short time series — we recommend XGBoost for 10s prediction given its speed advantage over LSTM inference latency at execution time, with LSTM available as ensemble layer for pattern context - FastAPI backend handling model inference, signal generation, and trade execution - Polymarket CLOB API integration for both paper trading validation and live deployment - Simple React or Streamlit dashboard showing live signals, open positions, P&L, and model confidence - Full source code with documented pipeline and model retraining instructions We'd love to walk through your data sources and risk parameters on a quick call — everything stays private. Are you available this week? Anthony Muñoz

@AITSoft

Hello, I understand you need a bot for Polymarket's short-term BTC price prediction using data like BTC spot price, VIX, token price, and order book details. My approach is to build a real-time data pipeline feeding into an LSTM or XGBoost model that predicts the probability of price rise in the next 10 seconds. I prefer LSTM for time-series due to its ability to catch short-term patterns, but I can also test XGBoost to compare. The backend will be in FastAPI with a simple frontend dashboard in Streamlit or React. I'll also integrate with Polymarket’s API for both live and paper trading. I’ve previously built crypto scalping bots and short-term prediction systems, focusing on fast response and accuracy. I estimate about 3 weeks to deliver the complete system, including model training, backend development, and frontend interface. What is the expected maximum latency allowed between predicting and executing trades on Polymarket? Best regards,

@Herculesz

Hi I have strong experience building real-time Python trading systems with FastAPI, WebSocket data pipelines, feature engineering, and short-horizon ML models for event-driven decisioning. The main technical challenge here is not just training a model, but making 10-second probability signals reliable enough to drive live execution against fast-moving Polymarket order book conditions with low latency and controlled slippage. I would approach this by building a streaming pipeline for BTC spot, token price, and order book features, then using XGBoost first because it is usually more stable, faster to iterate, and easier to validate than LSTM on ultra-short horizon classification. From there, I can expose the model through a FastAPI service, connect it to Polymarket CLOB logic for paper and live trade execution, and provide a simple dashboard for signal monitoring, trade logs, and strategy controls. I also focus on practical trading-system details like feature drift, latency-aware inference, position rules, and clean fallback behavior when market or API conditions degrade. The result would be a maintainable full-stack system covering data ingestion, model serving, execution flow, and monitoring in one connected architecture. I have worked on similar real-time automation and prediction systems where stability, speed, and clear observability matter just as much as the model itself. Thanks, Hercules

@iglobalvn

Unlock the power of precision trading with a custom Polymarket trading bot that scalps opportunities in real-time. With over a decade in machine learning and full-stack development, I am uniquely positioned to deliver a robust solution tailored to your needs. My expertise in LSTM and XGBoost will ensure accurate predictions of BTC price movements, while my proficiency in Python and FastAPI guarantees a seamless backend integration. I have successfully built data pipelines that ingest and process financial data, and I have experience working with APIs, ensuring smooth communication with Polymarket's CLOB. My approach involves an agile development cycle, focusing on iterative testing to fine-tune the model's accuracy and optimize the trading strategy. Let's discuss how I can turn your vision into a high-performing trading bot that maximizes profit potential. Looking forward to collaborating on this exciting project!

@corpmember29

LOW-LATENCY ML TRADING BOT FOR POLYMARKET — BUILT FOR REAL-TIME SIGNALS & SCALPING PRECISION I’ve built real-time crypto trading systems with ML-driven signals and exchange integrations, including short-horizon prediction pipelines and automated execution. Relevant Projects: BTC Scalping Bot (Binance): WebSocket pipeline + XGBoost model predicting 5–15s price moves, executed micro-trades with latency-optimized FastAPI backend. Options Signal Engine: LSTM-based short-term direction classifier with live dashboard (PnL, signals, execution logs). Model Choice: I recommend XGBoost over LSTM for 10s prediction: • Performs better on tabular + engineered features (order book imbalance, micro price moves) • Lower latency (critical for 10s horizon) • Easier to retrain and tune vs LSTM LSTM can be tested later, but XGBoost is more practical + faster for MVP. System Architecture: • Data: WebSockets (BTC price, order book, Polymarket CLOB) • Features: momentum, spread, imbalance, micro-volatility • Backend: FastAPI (low-latency inference API) • Trading Engine: paper + live execution logic • Frontend: Streamlit (real-time signals, trades, PnL) Data pipeline + ingestion Feature engineering + model training API + trading logic Dashboard + testing (paper trading) Result: Fast, iterative bot optimized for micro-scalping with controlled execution. Happy to discuss data sources & latency constraints before starting. Thanks

@planDapps

ULTRA SHORT SCALPING: POLYMARKET CLOB INTEGRATION & TIME-SERIES ML High frequency scalping on Polymarket’s binary markets requires more than a model it requires a low-latency execution pipeline that beats the spread. At Plan D Studios, we have 12+ years of experience in Software Architecture and Machine Learning (ML), specializing in real time WebSocket data ingestion and automated trade execution. Technical Strategy: Model Choice: I recommend an XGBoost regressor over LSTM for 10s predictions. While LSTM captures sequences, XGBoost handles high dimensional order book features (imbalance, tick level volatility) with significantly lower inference latency critical for 10s scalping. The Pipeline: A FastAPI backend processing 1s spot data vs. Polymarket CLOB depth, triggering limit orders via the Gnosis safe/CTF contracts. Relevant Projects: MEV-Lite Bot: A Python-based DEX arb bot using WebSockets to capture <1s price discrepancies. Sentiment Driven Scalper: Integrated News APIs with a Gradient Boosting model for rapid BTC/USDT scalp trades. Timeline: 3–4 weeks for a full paper trading MVP. Since Polymarket liquidity can be thin, should the bot prioritize aggressive market orders to capture the 10s move, or use passive limit orders to minimize slippage? Regards, Haider

@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

@c0909h1179

Hi there, I am the best here! Please check out my profile and see what others have to say about the work I've done related to the skills you're looking for. Hope to work together soon. Thanks!

@mubeenm5026

Having successfully developed several automated trading bots, including those in the cryptocurrency space, my team at Web Crest is well-equipped to create and implement a bot that trades on Polymarket's binary markets. Our experience includes thorough knowledge of data pipelines, LSTM and XGBoost models, backend APIs, and frontend dashboards. Moreover, we understand the unique requirements of real-time crypto trading that necessitates precision and adaptability. We have rich experience working with Python for real-time WebSocket pipelines as well as expertise in utilizing APIs such as Polymarket's or Gnosis Conditional Tokens - giving us an edge in this specific project. We always keep performance enhancement in mind while designing systems and our 10% edge in completion rate with consistent positive feedback showcases our commitment to producing reliable, scalable system. Our model choice will be between LSTM or XGBoost and this decision primarily rests on how easily training can be performed given the nature and volume of the available data. With these 10-second short-term predictions, we believe LSTM stands out due to its capability to effectively process sequential data within small timeframes.

@Sohail748

Hi there, I’m offering 25% off for this project while delivering a reliable, scalable trading system built for real-time decision-making. I’ve worked on trading bot systems involving real-time market data pipelines, strategy execution, API-based order handling, and dashboard monitoring. For this project, I’d build a modular solution covering data ingestion, feature engineering, model training, FastAPI backend, live/paper trading integration, and a clean dashboard for signals, trades, and performance tracking. For 10-second prediction, I’d lean toward XGBoost for v1 because it trains faster, is easier to tune/deploy, and often performs better on structured microstructure features like price change, spread, imbalance, momentum, and order book signals. If needed, we can later compare it with LSTM once the pipeline and baseline are stable. Relevant work includes real-time trading bot development with execution logic, broker/API integrations, historical/live data processing, and production-oriented backend architecture. I can also share similar automation and trading system examples in discussion. Estimated timeline: 10–14 days for a strong first version, depending on historical data availability and exact trading workflow scope. Regards, Sohail Jamil

@devendrathakur12

i’ve done very similar recently, building low-latency crypto scalping bots with WebSocket feeds and XGBoost inference. Do you need sub-second inference (<100ms) end-to-end including order placement? Are you planning to run this on a single node or distributed for redundancy? I suggest starting with XGBoost because it handles noisy short-term signals better and is faster than LSTM for 10s horizons. I also suggest strict execution guards (slippage + latency checks) because tiny delays can wipe profit. I will set up data pipeline (WebSocket ingestion, feature store), train/validate model, and expose via FastAPI. Then I will integrate Polymarket CLOB, add paper trading, and build a Streamlit dashboard with live metrics and controls. Best, Dev S.

@waqaswebmaster

Hi There A 10-second prediction bot lives or dies on latency, feature quality, and execution discipline — not just model choice. I’d approach this as a full trading pipeline: real-time ingestion of BTC spot, Polymarket prices/order book, feature engineering, fast probability inference, then controlled execution through paper/live trading with guardrails and monitoring. For this horizon, I’d start with XGBoost, not LSTM. On ultra-short windows, engineered microstructure features often outperform heavier sequence models while staying faster, easier to debug, and more stable in production. If the data proves otherwise, we can benchmark LSTM as phase two. I can build the stack end to end: Python data pipeline, FastAPI backend, model service, Polymarket CLOB integration, paper-trading loop, and a clean dashboard in Streamlit or React for signals, positions, PnL, and bot health. Estimated timeline: 2–3 weeks for MVP Phase 1: ingestion + feature store + baseline model Phase 2: paper trading + dashboard Phase 3: live execution hardening I can also structure the system so prediction, execution, and risk controls stay isolated for safer iteration. The first thing I’d want to inspect is the exact tick/data granularity you already have, because that will decide how much edge is realistically extractable at a 10-second horizon. best regards Waqas A.

@kishanbwts

I’ve built crypto scalping bots before that use real-time WebSocket data and similar ultra-short time series models. For example, I helped a trader predict 5-10 second price moves on ETH using an LSTM model combined with order book data, leading to consistent small gains. Given the 10-second horizon, I lean toward LSTM because it handles sequential dependencies well in noisy, high-frequency data, while XGBoost might miss temporal patterns here. But I’m open to testing both to see which performs best with your signals (BTC price, VIX, Polymarket prices). I’ll set up a live data pipeline to ingest BTC spot, VIX, Polymarket token prices, and order book updates via WebSockets. The backend will be a FastAPI service exposing model predictions and order execution commands. For frontend, Streamlit is a fast option to visualize live signals and trade history. One question: What’s your preference for paper trading? Should I fully mimic real order execution or simulate fills to speed up testing? Also, do you want support for multiple simultaneous markets or only the BTC 5-minute binary? I estimate 3-4 weeks to deliver a working prototype with live trading capability, including model training and dashboard. Ready to start building as soon as you confirm details.

@rbtech1984

Hello There!!! ★★★★ (ML trading bot for Polymarket BTC 10s prediction) ★★★★ Project understanding: ML bot for BTC 10s prediction with Polymarket CLOB, realtime data pipeline, backend and dashboard for execution and monitoring. Services: ⚜ BTC/orderbook data ingestion ⚜ XGBoost/LSTM model development ⚜ FastAPI backend integration ⚜ Polymarket paper/live trading setup ⚜ dashboard + backtesting system ⚜ risk control & execution logic Qualifications: 10+ yrs exp in Python, ML systems, crypto trading bots, FastAPI and real-time data engineering. Approach: start with XGBoost baseline for speed, then LSTM for sequence learning, compare performance and optimize latency before deployment. Closing: ready to start soon, happy to discuss details. Warm Regards, Farhin B.