Claude-Powered Stock Trading Bot

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Fully-Functional Trading Bot with Anthropic’s Claude Models ❇️ Hi My Friend, I hope you're doing well. I reviewed your project needs and see you are looking for a trading bot built around Claude models. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in automated trading systems. I will ensure the bot handles day-trading, swing-trading, and scalping strategies efficiently while providing well-documented code and a user-friendly interface. ➡️ Why Me? I can easily create your trading bot as I have 5 years of experience in algorithmic trading, focusing on Python development, API integration, and data analysis. My expertise ensures the bot runs smoothly with real-time monitoring and risk management features. I also have a strong grip on machine learning models and back-testing methodologies. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing with you in chat. ➡️ Skills & Experience: ✅ Python Development ✅ API Integration ✅ Algorithmic Trading ✅ Data Analysis ✅ Risk Management ✅ Back-Testing ✅ Real-Time Monitoring ✅ Modular Coding ✅ Documentation ✅ Performance Optimization ✅ Trading Strategies ✅ Machine Learning Waiting for your response! Best Regards, Zohaib

@nikhilgarg96

Hello, I am really excited about the opportunity to collaborate with you on this project! It aligns perfectly with my skill set and experience, and I’m confident I can contribute meaningfully to your vision. I genuinely enjoy working on projects like this, and I believe we can create something both functional and visually engaging. Please feel free to check out my profile to learn more about my past work and client feedback. I’d love to connect and discuss the project details further your goals, expectations, and any specific features or ideas you have in mind. The more I understand your vision, the better I can bring it to life. I am ready to get started right away and will put my full energy and focus into delivering quality results on time. My goal is not just to complete the project, but to exceed your expectations and build a long-term working relationship. Looking forward to hearing from you soon! With Regards! Nikhil

@Rekhathakur

Hello I have thoroughly reviewed your project description and am confident in my ability to assist you in completing it successfully. I believe it would be highly beneficial to delve deeper into the specifics of the job to determine the most effective way forward. I am open to scheduling an interview at your convenience, and I genuinely appreciate the chance to collaborate with you on this project. Your response is eagerly anticipated, and I'm excited about the prospect of working together. Thank you for considering my proposal. Looking forward to your prompt reply! Best regards Rekha!!!

@Herculesz

Hi I can build a modular Python trading bot that combines Claude-driven market interpretation with rule-based execution, risk controls, and broker API integration. The main technical challenge is preventing the LLM layer from making inconsistent trade decisions under real-time conditions, especially when day trading, swing trading, and scalping run in parallel. My approach is to keep Claude focused on structured tasks such as pattern classification, news sentiment, and trade-context scoring, while deterministic code handles signals, position sizing, order routing, and fail-safe risk logic. I have experience with Python automation, market data pipelines, broker API integrations, backtesting frameworks, and configurable strategy architectures designed for extension. The system can be structured with independent strategy modules, shared portfolio/risk services, centralized config management, and live monitoring through a lightweight dashboard or CLI. I would also build reproducible backtests, detailed trade logs, and configurable prompt/chain setups so each strategy’s rationale, PnL, and drawdown can be audited clearly. This gives you a maintainable trading framework where Claude enhances decision support without compromising execution discipline or system stability. Thanks, Hercules

@Feriver

Hello, I understand you need a fully-functional Claude-powered stock trading bot running day, swing, and scalping strategies independently. I will structure the system with Python, modular strategy layers, and clean code, integrating broker APIs for NASDAQ/NYSE and international markets. Each strategy will log PnL, drawdowns, and trade rationale, while Claude handles pattern recognition, sentiment analysis, and order timing. Deliverables include source code, environment files, Claude prompt configurations, back-test reports over one year, and a deployment guide for VPS/cloud setup. The system will allow configurable keys, endpoints, and model parameters from a single file. My approach ensures robust risk controls, real-time monitoring via dashboard/CLI, and extendable architecture for swapping models or tweaking strategies. Libraries like Pandas, NumPy, CCXT, and PyTorch (for model handling) will be leveraged for reliability and performance. I can also provide reproducible back-tests and clear logging to validate strategy efficacy. Thanks, Asif

@solutionsplayers

Hello, most “AI trading bots” fail because they over-rely on the model for decisions without strong execution and risk controls—leading to unstable results. You need a system where AI handles insight, but code enforces discipline. I’ll build this in Python with a modular architecture: data ingestion (market + news), strategy layer (day/swing/scalp), and execution via broker APIs (e.g., Alpaca/IBKR). Anthropic Claude will handle sentiment + pattern reasoning, while the system manages risk rules, position sizing, and order execution. Includes: backtesting engine (1+ year data), configurable strategies, logging (PnL, drawdown), and CLI/dashboard for monitoring. Result: a flexible, production-ready bot—not a black-box script. I can prototype one strategy (e.g., day trading) with backtest first to validate performance. Do you prefer starting with paper trading or going straight to live execution?

@quan9824

Hello, I can build this trading bot as a modular Python system that combines Claude-driven market interpretation with a robust execution and risk-management engine. I have experience designing AI-assisted trading workflows and structuring systems so the LLM handles higher-level reasoning such as pattern interpretation, news/sentiment review, and order-timing suggestions, while deterministic code manages market data, strategy orchestration, position sizing, drawdown limits, and broker execution. My recommended architecture would separate the project into: - Data layer for real-time and historical feeds - Strategy layer for day trading, swing trading, and scalping running independently - LLM decision layer using Claude prompts/chains per strategy - Execution/risk layer for broker API integration, order routing, stop logic, and fail-safes - Monitoring layer with CLI or lightweight dashboard for PnL, drawdown, logs, and rationale tracking For libraries/tools, I’d typically use: - Python - pandas, numpy, TA-Lib/vectorbt/backtrader - FastAPI or simple dashboard stack - ... I would build it with strong emphasis on maintainability, configurability, and safe execution controls, so you can later swap brokers, models, or strategy parameters without rewriting the core system. Let's discuss more detail.

@Ekarthaan

Hi Jay M., This is quite similar to a project I delivered last week, so I can jump straight into execution. Ready to start immediately. Two questions: 1) Which broker/data combo do you prefer (e.g., Alpaca+Polygon for easy paper/live US equities, or IBKR for multi-venue), and do you need L2/order book data for scalping latency targets? 2) Define the trading universe and risk guardrails: max leverage/exposure per symbol, max daily drawdown, order types allowed (MKT/LO/IOC), and any news sources you want prioritized (Benzinga, Finnhub, etc.). Two suggestions: 1) Use a hybrid design: quantitative feature generation and rule-based execution for latency, with Claude scoring pattern strength, news sentiment, and providing rationale/meta-labels. Hard risk engine gates every order; Claude never bypasses limits. 2) Build an event-driven, adapter-based core: pluggable brokers/data feeds, message bus (Redis/Kafka), and parallel strategy workers (Ray/multiprocessing). Central YAML config + pydantic validation; structured logs + TimescaleDB for metrics. Action Plan: Phase 1: Requirements lock, select broker/data, repo scaffold, single config schema. Phase 2: Data layer (realtime+historical), storage, feature pipelines. Phase 3: Strategy modules (day/swing/scalp), Claude chains, risk/position sizing; backtests (≥1y) with vectorbt/backtrader Best Regards, Sid

@GameOfWords

I’m a full-stack software engineer with expertise in React, Node.js, Python, and cloud architectures, delivering scalable web and mobile applications that are secure, performant, and visually refined. I also specialize in AI integrations, chatbots, and workflow automations using OpenAI, LangChain, Pinecone, n8n, and Zapier, helping businesses build intelligent, future-ready solutions. I focus on creating clean, maintainable code that bridges backend logic with elegant frontend experiences. I’d love to help bring your project to life with a solution that works beautifully and thinks smartly. To review my samples and achievements, please visit:https://www.freelancer.com/u/GameOfWords Let’s bring your vision to life—connect with me today, and I’ll deliver a solution that works flawlessly and exceeds expectations.

@FaizalShaik80

Hi, hope you are well. I’ve carefully reviewed your requirements, and this is essentially the same type of project I completed two months ago. I am an experienced and specialized freelancer with 6+ years of practical experience in C Programming, Python, C++ Programming and I’m able to complete and deliver this project promptly. You can visit my profile to check my latest work and recent reviews. Connect in chat to discuss details and next steps. Warm regards.

@HiraMahmood4072

Hello, With over 7 years of experience in Machine Learning (ML) and Python, I have carefully read the requirements for the Claude-Powered Stock Trading Bot project. To accomplish this project, I propose to first conduct a thorough analysis of Anthropic’s Claude models and their applicability to stock trading. I will then design and implement a trading bot that integrates these models effectively. The system will be capable of running day-trading, swing-trading, and scalping strategies simultaneously. I will ensure that the code is clean, well-documented, and modular, allowing for easy customization of strategies and parameters. The deliverables will include source code, environment files, Claude prompt/chain configurations, back-test reports, and a deployment guide. The bot will be designed to execute trades autonomously, log performance metrics for each strategy, and offer configurable settings for external keys and model parameters. I am keen on discussing the project further to understand your specific requirements and preferences. Please connect with me for a detailed conversation. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@ivaylo8

Hello there, I’m Ivaylo, and I’m excited to build a Claude-powered stock trading bot tailored to your day-trading, swing-trading, and scalping needs. I’ll deliver a clean, well-documented Python solution with a modular strategy layer that lets you swap models or tweak parameters without rewriting core logic. The system will: (1) ingest data from reliable feeds (NASDAQ/NYSE and international venues if desired), (2) run separate, parallel strategy modules for day, swing, and scalping, and (3) hand off timing signals to a robust execution layer with risk controls. Key components: - Modular architecture: clearly separated data, models, risk, and execution layers, with a single, configurable settings file for APIs, keys, and model parameters. - Claude prompts: a dedicated prompt/chain configuration per strategy, stored and versioned for easy iteration and auditing. - Real-time dashboard/CLI: live PnL, drawdown, trade rationale, and model status. - Backtesting: one-year historical data per strategy with metrics such as Sharpe, Max Drawdown, win rate, and turnover. - Deployment: containerized guide and VPS/cloud setup instructions. Deliverables include source code and environment files, Claude prompt configurations for each strategy, back-test reports, and a clear deployment guide to run on your preferred cloud. All external keys and endpoints are centralized in a single config file for ease of management. If you’ve worked with AI-assisted trading before, I’ll lean on proven

@danefoong

Hello, I can build this trading bot system for you. I have experience developing automated trading systems and integrating AI models into trading workflows. For your project, I would create a clean, modular Python-based system where day trading, swing trading, and scalping strategies run independently while Claude handles pattern recognition, sentiment, and timing. The system will include: * integration with a reliable broker (e.g. Interactive Brokers or Alpaca) * structured Claude prompts for each strategy * full risk management and execution layer * backtesting over at least one year of data * real-time monitoring via dashboard or CLI * single config file for all keys and parameters You will receive: * complete source code and setup files * Claude configurations for each strategy * backtest reports * deployment guide for VPS/cloud The bot will be able to run fully automated (paper or live trading), with clear logging of PnL, drawdown, and trade reasoning. Let me know if you’d like me to outline the architecture or timeline. Best regards

@ianb65

As an experienced Full-Stack Developer and Trading Automation Specialist, I bring a unique skill-set to your project that aligns perfectly with your requirements. With 7+ years in the field, I have successfully built similar trading bots utilizing Python, which is your preferred language. I have a deep understanding and extensive experience in incorporating AI models into algorithmic trading systems like this one. Lastly, but importantly, I understand your need for back-test reports glowing over at least one year of historical data. In this regard, my data analysis skills and familiarity with quantitative techniques will enable me to provide you accurate insights to validate vital trading decisions. All things considered, I believe together we can build not just a stock trading bot with intricate Claude model but also a long-lasting collaborative relationship. Don't hesitate to click ‘Invite’ to discuss how we can make this project a reality!

@nemanjac6

Hello, you’re looking to build a Claude‑driven multi‑strategy trading system, and I’d handle it by structuring a Python backend that orchestrates data ingestion, model prompts, strategy logic and execution pipelines cleanly. From your description, this involves parallel workflows, so I’d architect it with isolated async workers for day‑trading, swing‑trading and scalping. A key constraint is keeping latency low while maintaining accurate risk controls. I’ve delivered similar LLM‑assisted trading engines with modular strategy layers and configurable execution adapters. I’d break this into: • Backend services for market data, brokerage APIs (e.g., Alpaca, IBKR, Tradier) and execution logic. • A strategy module where Claude prompt chains run independently and emit signals validated by Python risk filters. • A monitoring layer (CLI or lightweight dashboard) streaming PnL, drawdown and rationale logs. One insight: structuring Claude prompts as deterministic chains greatly stabilizes signal consistency. Which brokerage API do you prefer for execution, or should the system support multiple adapters from day one? Let me know if you want this broken down further. , Nemanja

@MqlcoderHub

Hi there, I can build your trading bot around Claude models using a clear, modular structure: one layer for strategy logic (day, swing, scalping), one for execution and broker API integration, and one for risk management and logging. Python will handle data feeds, order placement, and dashboard/CLI monitoring, while Claude manages pattern recognition and news sentiment analysis. Quick questions: Which brokers or APIs do you want to integrate first for live trading? Should each strategy have independent risk settings, or a single risk layer across all strategies?

@seandinwiddie

https://www.freelancer.com/u/seandinwiddie Hi, I’m confident I can help you build a Python-based, modular trading simulation framework using LLMs like Anthropic’s Claude for strategy analysis, pattern recognition, and sentiment evaluation. With experience in algorithmic trading research and backtesting frameworks, I’ll ensure your system is fully testable and extendable without risk to live markets. I will start by designing a modular architecture separating data feeds, strategy engines, and LLM-driven analysis. Next, I will implement backtesting for day-trading, swing-trading, and scalping strategies using historical data, logging simulated PnL, drawdowns, and decision rationale. Finally, I’ll provide a CLI/dashboard to monitor simulations in real time, along with clean, documented code, configuration files, and a deployment guide for local or cloud execution. The result will be a research-ready framework where you can safely test strategies, tune models, and prepare for eventual live deployment with proper controls and licensing. Let’s discuss your preferred data sources and strategy rules so the simulation framework aligns with your goals. Looking forward to collaborating,

@AlexNdungu

Hello, This is exactly the kind of LLM + trading system integration I specialize in. I can build a modular, production-ready trading bot powered by Claude with clear separation between AI decisioning and execution. My approach: 1. Architecture Python async with: Market data layer with broker API integration Strategy engine considering day / swing and scalping running independently Execution engine for orders, risk controls, position sizing LLM layer of Claude for pattern recognition, sentiment, timing Config-driven setup of all keys, params, models in one file 2. Claude Integration Custom prompt chains per strategy: Day trading using momentum + intraday structure Swing using trend + fundamentals + news sentiment Scalping using microstructure + rapid signals Structured outputs of JSON to ensure deterministic execution 3. Features Parallel strategy execution with separate PnL + logs Backtesting engine for 1+ year historical data Risk management in max drawdown, position sizing, and stop logic CLI/dashboard for real-time monitoring 4. Deliverables Clean, documented source code Claude prompt configurations Backtest reports Deployment guide I can start immediately, feel free to reach out via chat and I’ll outline a quick architecture tailored to your preferred broker.

@arslanshahid386

Hi there, I see you're looking to develop a Claude-powered trading bot tailored for stock trading strategies like day-trading, swing-trading, and scalping. With 4+ years of experience in software development and machine learning, I can build a robust system that integrates Claude for pattern recognition and news sentiment analysis while ensuring smooth data feeds and risk management. My approach would involve creating a modular architecture in Python, allowing for flexibility in strategy adjustments and model configurations. Clean, well-documented code will be a priority, along with a dashboard or CLI for real-time performance monitoring. I can also help determine the best brokerage APIs to use, ensuring scalability for future enhancements. One question I have: what specific features or metrics would you like to see on the performance dashboard? Best regards, Arslan Shahid