Budget: 18000 USD Deadline: 40 days
If a strong tester is needed, I am ready to do it, but if it's about 100 +- hours of work, then the rate should be in the range of 15-17, that is, 1500-1700 dollars. Feel free to reach out.
Budget: 1000 USD Deadline: 7 days
Hello, I have been working on a crypto trading bot with GPT-4 integration for market analysis and automatic trading via the Binance API, achieving 73% signal accuracy over 3 months of testing.
I am curious about what specific two-model voting strategy you plan to implement for LLM - comparing GPT-4 and Claude's responses or another approach?
I suggest we get in touch; I will provide you with free technical consultation and we can outline a development plan + I will tell you about my team!
Budget: 990 USD Deadline: 14 days
Hello!
Your project fits perfectly with my main stack: asynchronous Python, LLM integrations, and Docker deployments. I have worked on similar systems — with routing between AI providers, comparing model outputs, and Telegram notifications.
Regarding the specifications:
**LLM module with two-model voting.** In a SaaS AI assistant, I have already implemented routing between providers (GPT-4 / Claude / DeepSeek / Mistral via OpenRouter) and systematically compared model outputs to choose the optimal one. Voting between two models with consensus checking is a natural extension of this logic, plus function calling for a structured signal format (direction, confidence, justification).
**CCXT + Binance Spot.** I have worked with CCXT, and the async wrapper connects as standard. I have experience integrating with exchange and payment APIs (CryptoCloud, TON Connect, Lava.top).
**Risk management.** Hardcoded rules (stop-loss, max position size, daily loss limit, cooldown after a series of losses) will be implemented as a separate layer before executing the order. This is critical: the LLM should not have the ability to bypass limits — its output goes through a validator.
**Telegram notifications.** This is my main tool; I have created dozens of bots using aiogram — signals, execution confirmations, errors, daily reports, emergency stops on command.
**Docker on VPS.** I deploy all production projects myself using Docker Compose, setting up health checks, auto-restarts, logging, and monitoring.
A few questions before I can provide a more accurate estimate:
1. What timeframe and how many trading pairs are planned? This affects Binance's rate limits and the cost of LLM calls.
2. Does the LLM analyze indicators/candles or news background/sentiment? This will determine the structure of prompts and the cost of context.
3. Does the bot execute orders automatically or send signals for manual confirmation?
4. Are there any requirements for backtesting the strategy before launching with real money?
An estimate of 80–120 hours seems realistic for an MVP with one or two pairs and a basic set of strategies. I am ready to start within a couple of days.
Budget: 1000 USD Deadline: 1 day
Hello! I have enough experience to implement this project, please write to me privately to discuss the details.
My rate is 250 UAH/hour, so for your project, it will be 25,000 UAH with a deadline of 2-3 weeks.
Budget: 1000 USD Deadline: 30 days
I have practical experience in developing Python services using exchange APIs, asynchronous architecture, AI integrations, and automated decision-making systems. In your project, the key complexity lies not in connecting GPT-4 or Claude itself, but in creating a reliable architecture where AI does not make chaotic decisions, but operates within a clearly defined trading system with risk control and signal filtering. This is the approach I use in development. I implement a modular structure for the bot, where market data, model signals, risk management, order execution, and logging are processed separately. This will allow for future scalability of the system, connecting new strategies, exchanges, or models without completely rewriting the project. I pay special attention to the security of API keys, fault tolerance, and stable 24/7 operation on VPS. Another important advantage will be the transparency of logic: all signals, reasons for entering trades, risk limitations, and bot actions will be logged and sent to Telegram. This will allow for real-time system monitoring and quick result analysis. If necessary, I can provide examples of similar AI integrations and architectural solutions upon request.
Work plan:
- Analysis of requirements and bot architecture design;
- Setting up the Python environment and project structure;
- Integration of Binance Spot via CCXT;
- Implementation of market data retrieval and signal processing;
- Connecting GPT-4 / Claude via API;
- Implementation of a two-model voting system;
- Creation of a module for filtering and validating AI signals;
- Development of a strictly defined risk management system;
- Setting up position, loss, and trade frequency limits;
- Implementation of Telegram notifications and logging;
- Development of a bot status monitoring system;
- Containerization of the project using Docker;
- Deployment and configuration of VPS;
- Testing stability, error handling, and edge-case scenarios.
Budget: 3500 USD Deadline: 30 days
Greetings!
Your task of creating a personal crypto trading bot with an LLM decision-making module is completely clear. The architecture with dual-model voting (GPT-4 + Claude) and hardcoded risk management is the most reliable approach for AI trading at the moment.
I have experience in developing asynchronous systems in Python, integrating LLM agents, and working with API frameworks.
How I will implement this project:
Exchange integration module (CCXT + Binance Spot):
Implementation on Asyncio to minimize delays in receiving market data (Orderbook, Clines) and placing orders (Market/Limit).
Secure storage of API keys through environment variables.
LLM Decision Module (Voting):
Development of asynchronous requests to OpenAI and Anthropic APIs with a clear JSON Schema output (Buy / Sell / Hold, target, stop).
Consensus logic: an order is opened only when signals from both models match (or according to your model weights). Optimization of prompts to minimize delays and token costs.
Risk management and Telegram notifications:
Extraction of risk management rules (Max drawdown, position size from deposit, strict Stop-Loss/Take-Profit) into an isolated independent module that blocks any incorrect commands from the LLM.
Logging all steps and sending instant alerts to Telegram (order status, balance, errors).
Infrastructure:
Containerization of the entire application via Docker (Multi-stage build for lightweight image).
Setup on your VPS with a 24/7 restart policy and log rotation.
Budget: 1000 USD Deadline: 30 days
Hello. I can take on the task.
I suggest doing everything step by step to build a stable foundation right away, rather than just creating a "bot for the sake of a bot." First — integration with Binance Spot via CCXT, trading logic, risk management, Telegram notifications, and a test mode without real trades. After that, we can connect the LLM module and the model voting logic.
I want to highlight an important point: I would not give GPT/Claude direct access to open trades. The model can participate in analysis and signal formation, but the final decision must go through strictly defined risk rules — limits, stop-loss, daily drawdown restrictions, number of positions, etc.
We can also do: — Docker deployment on VPS
— logging and monitoring
— automatic notifications in Telegram
— dry-run / paper trading before launching with real funds
The stack is clear: Python, CCXT, Binance API, Docker.
If there is a technical specification or an example of signal logic — feel free to send it, I will take a look and suggest how to best implement the architecture.
Budget: 2000 USD Deadline: 15 days
Good day! I present to you a team of professional developers specializing in AI solutions for business! We will create a turnkey solution for you 🙌
An experienced team, ready to offer special conditions for you. Send me a personal message, and I will show you our portfolio and website. I guarantee quality and fast work!
Budget: 1000 USD Deadline: 30 days
Hello! I have reviewed your task and believe that I can successfully complete it. I would be happy to collaborate, please write to me personally for further details.
Budget: 1000 USD Deadline: 14 days
80-120 hours will be spent only on backtesting the history, setting variables for dynamic formulas, and demo launch; it is clear that the author does not understand how this works.
I can show my bots, offer options with an extension of the terms and deadlines, feel free to write.
Budget: 1000 USD Deadline: 7 days
Good day. I would be happy to write this bot! I am also very interested in this topic. I have already written many bots for Binance. I will easily handle this task as well. Write to discuss...
Budget: 1000 USD Deadline: 10 days
Hello, Zengyixiao Zeng, I have repeatedly created various trading strategies, simulators, and so on in the field of trading and crypto. This includes creating based on LLM signals (I also used FinRL and similar solutions for utilizing LLM and AI in trading). Let's go step by step:
1. I have worked with Binance, so there will be no issues.
2. The solution module partially depends on you; it's clear how to implement the opinion and evaluation of two models. There are various ready-made skills. We have a rating of LLM models in trading. However, I think it's important that the main core is still yours.
3. For testing strategies, Nautilus Trader is incredibly suitable. Familiarize yourself with this tool. It will allow us to write any super realistic testing, stable. And then it will also be easy to transfer to the real market.
4. Finally, we will set up all the Docker and start testing.
I have the understanding and knowledge of how to implement this, check it as realistically as possible (paper trading), and launch it so that everything works smoothly - I am looking forward to working with you!
Budget: 1000 USD Deadline: 10 days
Good afternoon.
I am ready to discuss the project. I would start not with the code, but with the architecture and security rules of the bot, because in a trading bot, the most expensive mistake is not a bug in the interface, but a random order or an uncontrolled series of trades.
My approach is this: first, we create a test mode without real trades, logging every decision made by GPT/Claude, checking risk rules, limits, and the bot's behavior in contentious situations. Only after that can we carefully enable real trading through Binance Spot.
If this approach works for you, I am ready to discuss the details.
Budget: 2700 USD Deadline: 14 days
Hello, there are developments in the field, the assessment in the tasks is interesting, but we need to gather more data to start the work.
The private message is open, write.
Budget: 1000 USD Deadline: 36 days
Good day. I can write such a bot in Python + OpenAI + CCXT + Telegram Bot API + Deployment in Docker. I'm just not sure about 80-120 hours - it's better to do it slowly but with quality. I have already worked with the integration of ChatGPT. I would like to know more about the signal generation algorithm.
Budget: 1000 USD Deadline: 15 days
Good day, Zeng Yixiao!
Interesting stack. In such projects, the main difficulty usually lies not in connecting the GPT or Binance API, but in ensuring that the LLM does not turn into a generator of random solutions without risk control and predictable logic.
I have experience with AI integrations, automation, and decision-making systems, where it is important to separate trading logic, analysis, risk module, and model operations.
I can implement:
- CCXT + Binance Spot integration
- LLM module with two-model voting
- GPT / Claude integrations
- a robust risk management system
- Telegram notifications
- Docker deployment
- VPS setup
- logs and monitoring
I would separately include protection scenarios: drawdown limits, trade limits, signal filtering, decision logs, and a mechanism that allows analyzing why the model made a specific decision.
In terms of architecture, this is well divided into independent modules: market data acquisition, analysis, AI logic, risk control, and trade execution. This approach greatly simplifies support and further development.
I would be happy to collaborate :)
Budget: 1000 USD Deadline: 10 days
Good afternoon. Write to discuss, there are ready-made solutions, but they are a bit different from what you need, we will customize it for you.
Budget: 1000 USD Deadline: 7 days
Hello! I can implement a dual-model voting system, strict risk management rules, Telegram notifications, and deploy everything in Docker on a VPS. An estimate of 80–120 hours seems realistic for the MVP.
I have questions about the strategy: what pairs are we trading, what timeframe, is there already a signal logic or is that also part of the task?
I am ready to discuss the details.
Budget: 1000 USD Deadline: 7 days
Стек 1 в 1 наш: Python, CCXT/Binance Spot, LLM ensemble (GPT-4 + Claude), Telegram (aiogram), Docker/VPS. Релевантные кейсы: Winbix.AI (multi-tenant AI agent на FastAPI + Postgres, $2K MRR, 30+ клиентов), BrandSync AI (GPT-4 + Whisper + BullMQ pipeline в проде), BotFusion AI (Telegram SaaS, 19+ платформ).
Архитектура:
CCXT с retry и rate-limit handling, async wrapper над Binance Spot
LLM voting через aiohttp параллельно (OpenAI + Anthropic), консенсус с конфигурируемыми порогами
Risk module: max position, daily loss limit, обязательный stop-loss, cooldown после убытка, валидация перед каждым ордером
Telegram (aiogram): alerts по сделкам, daily P&L, /pause /resume команды
Docker Compose с healthchecks, structured logging, Prometheus metrics
Технические нюансы: idempotency keys против дублей при retry, Decimal вместо float для цен, WebSocket для partial fills.
Quentar, студия из 6 разработчиков (UTC+2). На проект: 1 senior Python + 1 для testing/observability, PM без дополнительной стоимости.
Портфолио: winbixai.com/en/startups.
Вопросы:
Временной горизонт сигналов (intraday/daily/swing) и сколько активов в портфеле?
Нужен paper trading режим перед live для верификации?
Жду в лс, есть еще пару вопросов
Budget: 1000 USD Deadline: 7 days
Good day, I am a Python developer with extensive experience in developing Telegram bots of various complexities. I can complete your order quickly and efficiently. Write to discuss the details; I am ready to start today. I will also upload your bot to the server for stable operation 24/7 and will provide technical support for the bot for another month. This is included in the cost. I have experience in developing trading bots.
Budget: 1000 USD Deadline: 3 days
Good day! I professionally develop trading robots in Python. I have experience with CCXT/Binance API, integrating LLM (GPT-4/Claude) for signal generation, risk management, and Docker/VPS. I am ready to implement your project with notifications in Telegram and 24/7 support. Message me privately, and we will clarify the details.
Budget: 1000 USD Deadline: 3 days
Hello! I have reviewed the project and am ready to start working. I am confident you will be satisfied with the result.
Budget: 1000 USD Deadline: 5 days
Good day. Over 3 years of experience in developing Telegram bots. I can guarantee the quality and speed of work!
After the approval of the technical specifications, I will start working.
