Nasdaq Futures Automated Trading System

@Guptapuru304

Hi — Puru here from ErgoSphere. You're essentially building an automated trading execution system for Nasdaq futures, where signals are parsed to execute trades via a brokerage account with configurable risk parameters and position management. What usually matters most here is the reliability of signal parsing and execution, and the robustness of risk management and fail-safes to prevent erroneous trades. For this project, I recommend a Python-based architecture with FastAPI for the backend due to its async capabilities, which are excellent for real-time data handling. Use WebSockets for market data, combined with a robust parsing engine that leverages deterministic rules and possibly LLMs for flexibility. Secure handling of API credentials through environment variables or secured vaults is crucial. I've worked on AI-driven trading bots, backend APIs using FastAPI, and secure broker integrations. A few questions to better understand the scope: Q1 – Which broker API are you currently considering for trade execution? Q2 – Do you have a preference for the LLM-assisted parser, or would you prefer a purely deterministic approach? Q3 – How important is the dashboard's real-time data update frequency for your trading strategy? A working prototype could reasonably be targeted within 6–8 weeks, followed by a polished release in roughly 12 weeks depending on broker API integration challenges. Looking forward to hearing from you.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Your Automated Trading Execution System for Nasdaq Futures ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an automated trading execution system for Nasdaq futures. Look no further; Zohaib is here to help you! My team has completed over 50 similar projects for automated trading systems. I will create a robust solution that reads trading signals, interprets instructions, and executes trades through your brokerage account seamlessly. ➡️ Why Me? I can easily build your automated trading execution system as I have 5 years of experience in trading software development, specializing in APIs, order management, and automated trading solutions. My expertise includes Python, C#, and integrating broker APIs. I also have a strong grip on real-time data processing and risk management. ➡️ Let's have a quick chat to discuss your project in detail. I can show you samples of my previous work and how I can add value to your project. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ Python ✅ C# ✅ Futures Trading APIs ✅ Broker APIs ✅ WebSockets ✅ Automated Trading Systems ✅ Order Management ✅ Position Reconciliation ✅ Reliable Message Parsing ✅ Risk Management ✅ Logging ✅ Dashboard Development Waiting for your response! Best Regards, Zohaib

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in API Integration, Software Development, Financial Software Development, Risk Management, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@ShaikhAneesa

Hi there, I understand that you need a production-grade automated NQ/MNQ futures execution system that converts incoming trading signals into validated broker orders, manages partial exits, runners, stops, targets, follow-up instructions, and configurable risk controls. I’m confident I can build the complete signal-to-execution workflow with strict validation so ambiguous signals are rejected rather than traded. My approach is to first build the Python signal-ingestion and parsing engine with deterministic rules, signal IDs, expiry, duplicate detection, and validation. Next, I’ll implement a broker-agnostic execution layer using the selected Futures API, WebSockets, bracket orders, position reconciliation, partial-fill handling, and configurable NQ/MNQ sizing. Then, I’ll build the position/risk engine for scale-outs, breakeven, trailing stops, exposure/daily-loss limits, slippage controls, kill switch, and follow-up commands such as “scale out” or “move SL.” Finally, I’ll add the dashboard, structured audit logs, paper-trading mode, reconnection/error handling, historical signal tests, and staged live deployment. You will receive the complete application, parser, broker integration, execution/risk engines, dashboard, paper environment, source code, documentation, and deployment instructions. Which futures broker/API and signal-delivery channel (Telegram, Discord, email, webhook, etc.) will be used? I’m ready to start immediately. Warm Regards, Aneesa.

@Mahihassi

With comprehensive expertise in both electrical engineering and software development, including a mastery of Python and API integration, I'm excellently positioned to deliver the superior performance and functionality you're seeking for your Nasdaq futures automated trading system. My advanced skills in firmware development and embedded systems design will ensure that your software accurately interprets the trading signals and executes trades correctly via your chosen broker. I fully understand the complexities involved in position management, and my proficiency with AI and machine learning can create an intelligent algorithm that allows the system to adapt to different market scenarios ensuring maximum profit. Additionally, my background in industrial automation guarantees that your system will incorporate essential safety controls. I am well-versed in handling emergencies and contingencies, which is vital when managing finances, especially with automated systems. With me on board, your trading software will be engineered to align with robust standards; it'll reject malformed signals and prevent accidental duplicate positions, maintaining error-free workflow throughout. Overall, what sets me apart as the ideal candidate for this project is my ability to manage a complete product development cycle. This includes iterating from ideation to prototyping to a final market-ready product—skills you might need even after this initial project.

@Hubready

Hello!, I am a US-based senior software engineer and I’d approach this Nasdaq futures system like a trading product, not just a script. The real pain point is usually the gap between a signal and safe execution: timing, slippage, failed API calls, and risk controls. I can close that gap with a practical build process: 1) define the signal logic and broker workflow, 2) build the Python execution engine with logging/retries, 3) add risk controls like position sizing, max loss, and kill-switches, and 4) test in paper/live-sim before any real routing. I’ve built automation and financial systems where reliability matters more than flashy code. For this project, I’d focus on making it stable, transparent, and easy to maintain so you always know why a trade fired and what happened after. Relevant examples of work: - a futures signal dashboard tied to broker APIs - an automated options workflow for a private trading team - a market-data ingestion and alert engine Could you please clarify the following questions to help me better understand the project? 1) Which broker API do you want to execute through, and is paper trading required first? 2) Do you already have the signal rules, or should I help implement them from scratch? 3) What risk limits are non-negotiable for you, such as max daily loss, contract sizing, or trading hours? If you want, I can turn this into a clean first version quickly and then iterate based on real test results. James Zappi

@n2rtechnologies

Hi, When signals arrive, are they coming from a single source (one messaging channel, one format) or multiple sources that might need different parsing logic? That'll shape how we structure the intake layer. We've built financial software and integrations before, so handling broker APIs and position management is familiar ground. The budget and timeline shown are just placeholders — I'll send actual numbers once we nail down signal sources and how complex your partial-close logic needs to be. Regards, Nurul Hasan

@Herculesz

Hi, This project requires a robust, real-time automated trading execution system capable of parsing dynamic signals and managing complex position scaling for NQ/MNQ futures. The main engineering challenge lies in reliably interpreting varied textual signals and safely orchestrating trade execution with strict validation and risk controls. I typically design modular architectures separating signal ingestion, parsing, validation, order execution, and position management layers to maintain flexibility and resilience. My experience building an NYSE day trading bot with configurable risk parameters and real-time API integration aligns well with your needs. I would approach follow-up instructions by associating them contextually with active positions using stateful tracking and strict parsing rules to prevent misinterpretation. Duplicate and malformed signals would be filtered through layered validation and idempotency checks. The system would include a comprehensive logging and monitoring dashboard to maintain operational transparency and enable quick intervention. Thanks, Hercules

@denisSolution

Hi, I'm Denis, a full-stack developer with experience building automated trading systems connected to live broker APIs, including futures platforms. I've worked on signal parsing, position management, and risk-control layers for algorithmic trading applications. I understand the core challenge: you need a position-management engine that safely interprets variable signal formats while maintaining strict risk control. This means robust signal parsing with fallback validation—rejecting ambiguous signals rather than guessing. I've built similar systems involving trader instruction parsing, live position tracking, configurable entry logic, position-scaling rules, broker API integration, and continuous monitoring infrastructure. My approach: - Build a signal parser with deterministic pattern matching for high-confidence signals and LLM assistance for edge cases, with strict validation gates before execution - Separate signal layer from broker execution layer for future flexibility - Create a position manager tracking open contracts, managing partial exits, trailing stops, and enforcing risk limits before any broker order - Implement full audit logging and duplicate protection via signal fingerprints and position state tracking I'd use Python with asyncio for the core system, following your testing progression: parser validation, paper trading, end-to-end testing, then gradual live deployment. I'm ready to start immediately. Let's discuss the details. Thanks, Denis.

@PINESCRIPTERR

Hi, I can build this as a modular Python system covering signal parsing, strict validation, broker execution, configurable scale-outs, trailing stops, reconciliation, logging, and a live control dashboard. Follow-up messages would be matched to active positions using instrument, source, timestamp, and trade state. Idempotency keys, exposure limits, slippage controls, daily-loss limits, and paper-trading tests would prevent duplicate or unsafe execution. Which broker and signal source will the first version use? Regards, Houssame

@gutibs

Hi! This is something we can definitely handle, and the level of detail you've put into this brief makes it clear you know exactly what you need — which makes our job easier. Before I put a firm proposal together, the one thing that changes the architecture most is the broker. Do you already have an account with a specific broker, or are you open to recommendations? The answer determines how we build the execution layer — Interactive Brokers, Tradovate, Rithmic and AMP Futures each expose different APIs, and some are better suited to the kind of bracket + scale-out logic you're describing. On how we'd build it: Python is the right call here — clean separation between signal ingestion, the position management engine, and the broker execution layer so each can be swapped or tested independently. For the signal parser, we'd combine a rules-based layer for well-structured signals like "SHORT NQ 20750" with an LLM-assisted fallback for the looser follow-up instructions like "take partials" or "hold runners" — with strict validation gates before anything touches a live order. The dashboard would be a lightweight web interface showing connection status, live position, P&L and full controls. The staged testing approach you outlined — parser → paper account → small live → full — is exactly how we'd want to run this. Happy to go deeper on the broker question and the architecture from there. Gustavo & the DoTheCode team

@JustinJcob

I got you! Your NQ/MNQ signal-to-execution system needs a safety-first architecture, not a simple bot, and I can build it with parser, validation, broker execution, position management, risk controls, logging, dashboard, and paper-to-live rollout. I’ve built automated execution workflows with broker APIs, bracket/stop/limit logic, partial exits, reconciliation, duplicate protection, and fail-safe controls. I’d recommend Python or C# with modular layers: signal ingestion/parser, risk engine, OMS, broker adapter, dashboard, and audit logs. I’m young, a fast learner, available 24/7, and ready to handle the project specifics. Get the demo first before you pay. Which broker/API are you leaning toward, NinjaTrader/Tradovate/Interactive Brokers/other? Also, will signals come from Telegram, Discord, email, SMS, or another source? Let’s chat and discuss your answers, timeline, and cost clearly. Kind regards, Haroon Z

@C0RETECHIES

Hi, You need an autonomous execution pipeline for NQ/MNQ futures that converts raw text signals into validated broker orders. The core challenge isn't just the initial entry, but stateful position management: associating follow-up signals (scaling out, adjusting SL) to active trades, enforcing entry parameters (slippage, market vs limit), and maintaining hard risk constraints (max loss, duplicate protection) before API execution. Technical approach: - Python (FastAPI) backend for robust, low-latency processing. - Redis for real-time state management (tracking active positions, partial fills, and trailing stops). - Hybrid Parser: Regex for standard formats, mapping to a structured LLM (GPT-4o) for ambiguous follow-ups, strictly validated against a JSON schema before execution. - Broker Layer: Abstracted adapter pattern (e.g., Tradovate/IBKR) so the broker can be swapped without rebuilding the parser. - React.js frontend connected via WebSockets for the control panel and Kill Switch. Core modules: - Signal Ingestion: Deduplication, expiry logic, and entry zone validation. - Position Engine: Manages complex lifecycles, runners, and scale-out logic. - Risk Guard: Pre-trade validation against daily loss, max contracts, and exposure limits. We will use a phased rollout: isolated historical parsing validation, paper-trading integrations, failure testing, and finally small-size live deployment. Regards, Rohit

@mayr81

Build an automated Nasdaq NQ/MNQ trading execution system that turns incoming text signals into validated, broker-safe orders. Core workflow: Signal ingestion → robust parser → rule-based validation (slippage, expiry, slippage limits, duplicate protection) → broker execution → position-management engine for scale-outs, breakeven moves, and runner trailing/targets. Position management supports multi-stage outcomes (e.g., 6 contracts: close partial at +70, move remaining stop to breakeven, then configurable further actions). Follow-up commands like “Scale out”, “Move stop to breakeven”, “SL to 20800”, “Hold runners”, “Exit all” will be mapped to the existing live position using deterministic state tracking, not guesswork. Safety & controls: max contracts/exposure, max daily loss, max trades/day, reconciliation with broker positions, order rejection/partial-fill handling, connection failure recovery, and a kill-switch “CLOSE ALL”. Full timestamped audit logging from raw signal through fills and position modifications. Architecture will separate signal parsing/validation from a swap-in broker execution layer (so broker changes don’t require rebuilding the parser). Include testing stages with parser tests, paper/simulation environment, and progressive end-to-end validation before any live deployment, plus a simple dashboard (broker status, positions, P&L, stops/targets, signals, orders, daily loss, enable/disable trading).

@AndrewJSMAN

I've built live futures execution systems integrating Interactive Brokers and Ninjatrader APIs with real-time signal parsing and multi-leg position management, so this is directly in my wheelhouse. I'll architect this as a modular stack: Python backend (asyncio + websockets) handling signal ingestion with deterministic parsing + LLM validation layer, a state machine for position lifecycle (entry → scale-out → exit), and broker abstraction layer (IB/Ninjatrader initially, swappable later). Risk controls sit at the core—duplicate detection via signal hash + timestamp, slippage validation before order submission, reconciliation loop against broker positions every 5s, and kill-switch via Redis flag. Dashboard is React/FastAPI polling position state and P&L from SQLite audit log. Development flow: parser unit tests → paper trading against historical signals → live sandbox with small size → full deployment. I handle the architectural safety design with you—entry logic, scale-out rules, breakeven triggers all config-driven. Source code, full documentation, and deployment runbook handed over complete. I can start immediately. Thanks, Andrii.

@gowthamsarathy

1. Yeah I've built trading bots such as trading bots for NASDAQ, MT5 Trade copier and crypto trading bot 2. I've worked with IC markets 3. Yeah I know nq mnq and I'm a trader by myself. I trade nq MNQ GC MGC 4. We'll discuss later 5. We'll discuss further 6. We'll discuss further 7. I'd recommend using python language 8. Devlopment time 10 days 9. Development cost - will let you know after knowing the full scope 10. Yes I can provide the ongoing support

@salmanullahbaig

As a seasoned Python and AI Engineer with significant expertise in financial software development, I possess the technical and strategic skills needed to design and construct your Nasdaq Futures Automated Trading System. I thrive at the intersection of AI, data, and backend engineering, creating scalable, intuitive systems that work even in high-pressure, real-world settings. Over the years, I've cultivated capabilities in building API-driven platforms that automate complex tasks intelligently. In terms of trading projects specifically, I've built crypto trading bots, strategy optimization models, and multi-timeframe analysis systems - all fundamental skills for designing your desired system. My proficiency in AI and machine learning will ensure precise parsing of your trading signals and effective management of positions just as you envision it. I also appreciate the importance of scalable backend systems to handle high-frequency trading, which is why I emphasize clean architecture and high-performance APIs.

@mojjammil

Hi, this is mojjammil. The key here is treating signal interpretation and broker execution as separate steps, with strict validation between them so an unclear message never becomes an unintended order. I’d build the parser and position state first, then connect the broker through a paper account and test fills, reconnects, partial exits, duplicate signals, and recovery before considering live execution. The tricky part is keeping the internal position state synchronized with the broker when orders are partially filled, rejected, or modified externally. Which broker/API do you currently have access to, and does it provide both paper trading and real-time order/position updates? For follow-up messages like “scale out” or “move SL,” what identifier or timing context can reliably associate them with the original trade? If we're aligned, I can outline the implementation phases before kickoff. Best regards

@Aaleen110

The core problem is parsing those text signals and translating them into broker commands. I would use Python for this, specifically libraries like `re` for pattern matching to pull out the symbols, directions, and prices. The execution part means connecting to a brokerage API, I'd use their documented REST API for placing orders, submitting `SHORT NQ 20750` would translate to an API call to place a sell order on the NQ contract at 20750. I'd build a simple state machine to manage the position after execution. I will not be building a custom charting interface or anything beyond the direct signal to execution pipeline. Others might offer to build a full dashboard, that is not the core need here and would add complexity. What is the specific format of the text signals beyond the example, how variable are they. For what it is worth, every job I have taken on Freelancer has gone out on time and on budget, 100% on both. Let’s have a short call on Freelancer to cover the exact API endpoints for order submission and the expected latency for signal reception.