Analytical bot for Polymarket US & Kalshi

Budget: 500 USD Deadline: 10 days

Hello.

I am developing bots for Telegram using NodeJS. I am ready to take on the project. Write to me, and we will discuss.

Budget: 9000 USD Deadline: 10 days

Hello! I have created Telegram bots using aiogram that pull market data via API and send alerts based on conditions — parsing quotes, volumes, tracking large transactions. Kalshi has an official API, and we will take Polymarket through their API/on-chain; real-time data can be collected easily. The question is about the analytics: does the EV bot take the calculated probability of an event from your model/formula, or should it assess it itself, for example, based on the consensus of several platforms? This affects the entire logic of the profitability of the bet — I will implement arbitrage and volume movement in any case. I will provide more precise timelines and pricing once we finalize this.

Budget: 1250 USD Deadline: 10 days

Good day! I develop in Python, have worked on similar projects with React/Node.js, and am ready to collaborate.

Budget: 25000 USD Deadline: 30 days

According to the stack - Python is suitable. Aiogram for the Telegram bot, official APIs where available, Playwright should be kept as a backup rather than as a foundation. The estimate for the first working stage is 180,000 UAH and 30 days.

Similar cases from Ingello
> https://business.ingello.com/vorfahr - automation and analytics around data
> https://business.ingello.com/fractal - complex logic of agents and event processing
> https://business.ingello.com/forma-bpm - managed processes, notifications, and roles

Main page for FLH
> https://systems-fl.ingello.com

For the implementation software, I would create not just a bot, but a signaling system - data collection, market normalization for Polymarket US and Kalshi, history storage, EV calculation, arbitrage search, tracking large transactions, and a separate anti-spam layer for notifications. Note, the nuance here is the complexity in the quality of signals, not in sending messages in Telegram =)

What I need from you are API access, a list of market categories to start with, risk rules, and an understanding of where we get the calculated probability of the event. At the first stage, we can limit the number of markets, check signals on history, and not build a spaceship out of a coffee grinder.

Please clarify
> which market categories we take first - politics, sports, finance, crypto, or all at once
> is the calculated probability already in your formula or do we need to design it? Expand

Budget: 450 USD Deadline: 7 days

Analytical bot for Polymarket US & Kalshi
C#, Python — incorrectly specified categories?
Description
Bets
1
Discussion
Analytical bot for Polymarket US and Kalshi
Goal: Create a Telegram bot that collects data from Polymarket US and Kalshi in real-time, analyzes it based on specified algorithms, and sends notifications with betting recommendations.

1. Main functions (Parsing and Monitoring)
Data collection from Polymarket US & Kalshi: The bot should track active markets, odds (stock prices), trading volumes, and liquidity changes.

Additional sources: Ability to connect data from other sources (e.g., Twitter/X for tracking news or other prediction exchanges for comparing odds).

2. Analytical block
Instead of simply copying data, the bot should consider:

Expected Value (EV): Comparison of the current price on Polymarket US & Kalshi with the calculated probability of the event.

Smart money movement: Monitoring large trades (whales) that may indicate insider information or a strong trend.

Arbitrage situations: Searching for differences in odds between different platforms.

3. Telegram notification logic
The bot should not spam. Messages should include:

Event name and link to the market.

Current odds and recommended side of the bet.

Brief justification: Why the bot considers this bet advantageous (e.g., "Sharp increase in volume with stable price" or "Deviation from market expectations by 5%").

4. Technology stack (recommendation for the developer)
Programming language: Python (libraries Aiogram or Telethon for the bot, Requests or Playwright for parsing).

API: Use of the official Polymarket US & Kalshi API (if available) for the fastest data retrieval speed. Expand

Budget: 2000 USD Deadline: 7 days

Hello, I worked on the Crypto Arbitrage Monitor — the bot tracked price differences between exchanges, analyzed whale movements, and sent signals to Telegram. It processed 50+ trading pairs with a delay.

Budget: 2200 USD Deadline: 5 days

Hello
I am ready to take on the work today
I have a lot of experience in this
I will do everything beautifully and quickly

Budget: 500 USD Deadline: 15 days

The project is cool. Combining Polymarket and Kalshi is currently a hot topic, but the main thing is not to compromise on speed. If we scrape the pages "head-on," the bot will send signals when the odds have already changed and the bet will lose its meaning. I will write a proper asynchronous backend in Python that will connect directly to their official APIs and WebSockets. Data on prices and whale orders will be streamed in real-time. The math for calculating EV and finding arbitrage will be cached so that the bot doesn't lag, doesn't spam nonsense, and only issues notifications when there is actually a percentage gap or large money comes in. I will make the code clean and modular so that later we can easily add either PredictIt or a web panel. I will package everything in Docker and deploy it on your server to ensure it runs stably 24/7.

Budget: 1000 USD Deadline: 30 days

Good day. I have experience in writing algorithmic trading bots on different platforms. For an accurate budget and timeline, I need to familiarize myself more closely with the technical specifications. I am ready for cooperation.

Budget: 1000 USD Deadline: 15 days

Hello, there is already a trading (snipe/copy/predict/) + signal bot for Polymarket. I will adjust the configuration to meet your needs. I sent the bot screenshots privately.

Budget: 350 USD Deadline: 10 days

Hello, Yana!

The task of developing an analytical monitor for Polymarket and Kalshi is very clear. Creating an algorithmic sniper bot for prediction markets is a great engineering challenge. It is critically important to separate the data collection layer from the analytical core so that the bot has no latency and provides quality signals without spam.

I have extensive experience in developing asynchronous architectures in Python (aiogram 3.x + FastAPI) and building data monitoring systems with SQLite/PostgreSQL database integration. I am ready to implement a stable solution for you:

1. High-availability data collection (Low Ping): Instead of slow web scraping, I will set up operations through official APIs (Polymarket CLOB API and Kalshi API) using asynchronous WebSockets. The bot will receive updates on order book, liquidity, and trading volumes in real-time within milliseconds. Playwright will be kept exclusively as a backup module for bypassing geo-restrictions (if US proxies/VPN are needed).
2. Mathematical and Analytical Block: At the backend level, I will implement custom computational modules:
- EV Calculation: The script will compare current stock prices (odds) with your mathematical model of calculated event probability.
- Whale Sniper: The bot will analyze large orders in the transaction stream, detecting abnormal accumulation of positions by large players before a major trend change.
- Arbitrage Module: Monitoring the spread (price differences) on the same events between Polymarket and Kalshi to capture risk-free situations.
3. Smart Notifications (Anti-spam filter): I will implement a trigger threshold system based on SQLite. The bot will send a structured signal (link, odds, bet side) only when the volume or EV anomaly exceeds your specified limit (for example, a deviation of 5%+).

I deploy all projects on autonomous hosting Railway in Docker containers, ensuring the scripts run 24/7 without failures. You can view examples of my working bots under load and parsers in my profile.

I suggest starting with the development of a stable MVP (data collection from official APIs + basic EV and arbitrage analytics). Please let me know if you already have a ready mathematical formula/script for determining the calculated event probability, or if we will design its algorithm together based on historical data? Expand

Budget: 500 USD Deadline: 10 days

I reviewed the description - data parsing from two platforms plus analytics on EV and whale movements, this is no longer just copying quotes. I have experience with aiogram and real bots under load (Polymarket trade parser with insider logic (you can view it in my profile)), so the architecture on asynchronous requests and the database is familiar to me. The only point that is unclear from the post: do you plan to parse data from Twitter through the official API or are you open to web scraping? This affects the update speed and the cost of integration.

Budget: 1000 USD Deadline: 2 days

Hello, I have a similar bot but for a different platform. I can adapt it for Polymarket US & Kalshi in a couple of days. I would be happy to collaborate.

Budget: 360 USD Deadline: 10 days

Good day. I can take on the development of such a Telegram bot in Python.

I have experience with Python, automation, data processing, APIs, and AI/analytical tasks. I can implement data collection from Polymarket/Kalshi via API or parsing, basic analytics on EV, volumes, liquidity changes, arbitrage situations, and set up Telegram notifications without spam.

I suggest doing it in stages: first an MVP with market monitoring and notifications in Telegram, then adding more complex analytics, Twitter/X, or other sources.

Estimated time for MVP: 7–10 days.
Budget: starting from 18,000 UAH, final amount depends on the number of markets, data sources, and complexity of algorithms.

I am ready to discuss the details and propose a structure for implementation.

Budget: 25000 USD Deadline: 25 days

We already have a practically ready similar solution that can be quickly adapted for Polymarket US and Kalshi and discussed on the exchange now - I'm available.
It can be done in Python, Aiogram, using official APIs where available, with backup collection through Playwright and a separate anti-spam layer.
The target for the first working release is 85,000 UAH and 25 days; initially, we can include market monitoring, EV, large trades, arbitrage, and Telegram notifications without spam.
An important nuance is that for the quality of recommendations, data collection needs to be separated from analytics, and we must store the history of prices, volumes, liquidity, and signals; otherwise, the bot will simply be reiterating the market.
Cases with similar logic:
- https://business.ingello.com/fractal - agency automation with data processing and decision rules
- https://business.ingello.com/vorfahr - a system with AI logic, integrations, and a fast hypothesis testing cycle
- https://systems-fl.ingello.com - our landing page on systems and automation
Questions for evaluation:
- Do we need to monitor all markets or only selected categories and markets with minimal volume?
- Do you provide the formula for calculating probability and EV thresholds, or do we build it together based on historical data and tests? Expand

Budget: 700 USD Deadline: 25 days

Good day!

I can implement this project.

Cost: 35,000 UAH
Deadline: 20–25 days.

Before starting, I would like to clarify a few details: is there a ready algorithm for calculating EV and determining signals, or does it also need to be developed? This affects the final cost estimate and deadlines.

I would be happy to discuss the details and propose an optimal solution.

Budget: 300 USD Deadline: 4 days

Good day
I am ready to take on your project
I have experience working with complex tasks
Write to me, I will be happy to complete it

Budget: 100 USD Deadline: 1 day

Hello. I propose to implement a project with a modular, highly available architecture that will use asynchronous Python for efficient real-time data collection from official APIs and custom parsing mechanisms, ensuring data integrity and low latency. The main analytical algorithms for calculating mathematical expectations, tracking "smart money," and detecting arbitrage situations will be implemented with a focus on computational efficiency, accuracy, and minimizing false positives. I have significant experience in developing similar high-performance analytical systems and trading bots, using ready-made templates for data processing and reliable notification mechanisms, which will significantly speed up the development process. I suggest discussing all implementation details, final budget, and timelines in private messages.