AI-Auto Trading Signal System Development

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you need an AI-driven intraday trading signal system that learns directly from market data, detects changing regimes like trending or ranging conditions, and produces real-time Buy/Sell/Hold signals for instruments like Gold and BTC with alerts and dashboard visibility. I would approach this by building the system in layers: reliable data ingestion first, then regime detection and model training, followed by backtesting, risk controls, and finally a lightweight real-time dashboard with Telegram/email alerts. I’ve worked on trading automation and data-heavy systems where model reliability, latency, and clean validation pipelines were critical before anything touched live conditions. I’d be happy to go through the details and suggest the best technical approach. I have a few questions to get a better understanding: Q1 – Do you want the first version focused on signal generation and backtesting only, or should it also be structured for future broker/exchange execution from day one? Q2 – Which market should be prioritized first for model tuning: BTC, Gold, or both in parallel? Q3 – For the dashboard and alerts, do you want only the latest signal and confidence shown, or also trade history, backtest metrics, and regime-change logs? Looking forward to hearing from you.

@corpmember29

Hi, I can build this as a full AI-driven trading signal system with real-time market inference, backtesting, and deployment-ready architecture. I’ve worked on similar Python-based trading systems using ML models, live data feeds, and signal dashboards. What I will deliver: • Python-based AI trading engine (modular architecture) • Live data ingestion (Binance / Alpha Vantage / Yahoo Finance / CCXT) • ML models (LSTM / XGBoost) for signal generation (Buy / Sell / Hold) • Market regime detection (Trending / Ranging / Volatile) • Intraday signal processing (1m / 5m / 15m timeframes) • Risk module (stop-loss, position sizing, confidence scoring) • Backtesting framework for historical validation • Streamlit dashboard for live signals + market status • Telegram/email alert system for trade signals • PostgreSQL storage for historical + model data System design will be fully data-driven (no hardcoded strategy), with periodic retraining on new market data to improve accuracy over time. I can also structure it so it’s ready for future expansion into automated execution if needed later. Happy to start immediately and share a phased delivery plan for MVP → live system.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build an AI-Based Auto Trading Signal System for Profitable Trading ❇️ Hi My Friend, I hope you are doing well. I reviewed your project requirements and I see you are looking for an AI-based auto trading signal system. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in AI trading systems. I will create a system that generates buy/sell signals based on live market data and adapts to changing conditions. ➡️ Why Me? I can easily build your AI trading system as I have 5 years of experience in machine learning and data analysis. My expertise includes Python programming, model training, and real-time data processing. I also have a strong grip on technologies like TensorFlow and PostgreSQL, ensuring a robust solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail and let me show you examples of my previous work. I look forward to our conversation! ➡️ Skills & Experience: ✅ Python Programming ✅ Machine Learning ✅ Data Analysis ✅ API Integration ✅ Real-Time Data Processing ✅ Model Training ✅ Risk Management ✅ Backtesting Techniques ✅ Database Management ✅ UI Development ✅ Telegram Notifications ✅ Trading Strategies Waiting for your response! Best Regards, Zohaib

@LiveExperts

Hello, "With your ambitious vision of building an AI-enabled Auto Trading Signal System, I believe that my team at Live Experts LLC is tailor-made to meet your needs. Our proficient command in Python, scikit-learn, TensorFlow, and PyTorch in ML coincides perfectly with your required tech stack for developing the AI signal engine. We also bring with us extensive experience in data analysis which is crucial for successful model training. In fact, we have mastered machine learning models like LSTM and XGBoost that align directly with your project goals of generating dynamic Buy/Sell signals without any hardcoded strategies." "Adapting to live market conditions is no easy feat. Understanding this significance, we have built a comprehensive understanding of intraday markets across different timeframes and asset classes. This broad knowledge will be instrumental in integrating OHLCV data as well as news/sentiment feeds (if needed) into the system for a holistic view of market conditions." " Beyond these compelling abilities, our portfolio resonates well with your requirement of bringing the AI model to life for intraday trading and providing its output through a clean and simple UI. We have successfully created similar user-friendly dashboards using Streamlit. Lastly, we take high regard in maintaining a seamless and risk-free environment which is crucial for your trading platform. Thus, I assure you that my team at Live Experts LLC will Thanks!

@Herculesz

Hi Your project is technically exciting because the hardest part is not just generating signals, but building a system that adapts to regime shifts without overfitting noisy intraday data. I work with Python, TensorFlow, PyTorch, scikit-learn, XGBoost, CCXT, PostgreSQL, and Streamlit to build real-time ML pipelines for market data, signal generation, backtesting, and alert delivery. A major problem in systems like this is that models can look strong in backtests but fail live because market conditions change faster than the training assumptions. I solve that by combining regime detection, rolling retraining, strict feature pipelines, and a validation framework that separates trending, ranging, and volatile conditions before signals are released. I can build the full stack including live OHLCV ingestion, model training, buy/sell/hold inference, confidence scoring, dashboard display, Telegram or email alerts, and a backtesting layer with risk controls. The architecture will stay modular so crypto and gold can run under the same engine while respecting different market sessions, liquidity patterns, and execution logic. I focus on clean, scalable implementation and practical model behavior rather than promising unrealistic accuracy from purely data-driven intraday trading signals. Thanks, Hercules

@vishal1145

Your AI trading system will fail in live markets if it doesn't account for slippage, latency, and regime shift detection before the model retrains. Most ML models I've seen break during high volatility because they're trained on static historical data without real-time feedback loops. Before architecting this, I need clarity on two things: What's your expected signal frequency? If you're generating buy/sell alerts every 5 minutes on 1min candles, you'll burn through API rate limits on Binance (1200 requests/min) and Alpha Vantage (5 calls/min on free tier). This requires caching layers and WebSocket streams, not REST polling. What's your risk tolerance for false signals? LSTM models have 60-70% accuracy in backtests but drop to 45-50% in live conditions due to overfitting. Do you need ensemble models (LSTM + XGBoost + sentiment analysis) with weighted voting, or are you comfortable with a single model that retrains nightly? Here's the execution plan: - PYTHON + CCXT: Build WebSocket listeners for Binance and Alpaca (for Gold futures) to stream tick data without hitting rate limits. Store in TimescaleDB (PostgreSQL extension optimized for time-series). - LSTM + XGBOOST ENSEMBLE: Train separate models on trending vs ranging regimes using ATR and Bollinger Band width as regime classifiers. Route signals through a voting system that requires 2/3 model agreement before triggering alerts. - ADAPTIVE RETRAINING: Implement online learning with a sliding 30-day window. Model retrains every 6 hours using recent data, but only deploys if validation accuracy beats the current production model by 5%. - RISK MODULE: Calculate position size using Kelly Criterion based on model confidence score. Auto-generate stop-loss at 1.5x ATR below entry and take-profit at 2:1 risk-reward ratio. - STREAMLIT DASHBOARD: Real-time signal feed with regime indicator, model confidence heatmap, and P&L tracker. Telegram bot sends alerts only when confidence exceeds 75%. I've built 3 algorithmic trading systems for hedge funds that processed $2M+ daily volume. The difference between a research notebook and a production system is handling edge cases like exchange downtime, data gaps, and flash crashes. Let's schedule a 20-minute call to walk through your backtesting assumptions and data pipeline before we start coding.

@mahig47

Hello! As per your project post, you’re looking to build an AI based Auto Trading Signal System that generates real time buy, sell, or hold signals for instruments like Gold and BTC using live market data and adaptive models. The system will process price feeds, detect market conditions, and provide actionable insights through a simple dashboard and alerts. The goal is to deliver a reliable, data driven platform that adapts to market behavior and supports intraday trading decisions. My focus will be on delivering a complete AI trading signal system, featuring: live data ingestion from trading APIs with OHLCV processing, machine learning models for signal generation based on historical and real time data, adaptive market regime detection for dynamic strategy adjustment, intraday signal generation across multiple timeframes, and a dashboard with signal display, confidence scoring, and alert notifications via Telegram or email. I specialize in full stack development with strong experience in building data driven platforms, API integrations, real time systems, and scalable backend architecture using Python React and PostgreSQL. My focus is on transforming complex market data into clear actionable insights with reliable performance and intuitive user experience. Let’s connect to review your trading goals, target instruments, and signal usage workflow so we can define a clear MVP roadmap and execution plan. Best regards, Nikita Gupta.

@pinkeegupta

Hi I have read your requirements and I am sure I will be able to help you. May I know it will be web based or mobile App? Please message me so that we will have detail technical discussion. I have 9+ years of combined experience in Mobile Application development, Website development, Desktop application development, 3rd party Artificial Intelligence api, AR/ VR, Chatbot, Blockchain- Cryptocurrency, CRM & ERP, Game Development and any other Software development. Please consider me and initiate a chat for further detailed discussion. Regards, Anju

@devendrathakur12

i’ve done very similar recently, building intraday signal systems with CCXT + XGBoost/LSTM and live dashboards. Which broker/feed will you use for gold since it’s not native in CCXT? Do you want signals only or auto-execution hooks later? I suggest starting with XGBoost + engineered features because it stabilizes faster on 1–15m data. I also suggest a separate backtesting engine with walk-forward validation because it avoids overfitting and gives real confidence. I will first set up ingestion (CCXT + external gold API) into PostgreSQL with clean OHLCV. Then I will build features, train models, and add regime detection + backtesting. Finally I will expose signals via FastAPI + Streamlit with alerts and risk module. Best, Dev S.

@rbtech1984

Hello There!!! ★★★★ (AI-driven trading signals with adaptive models & real-time intraday insights) ★★★★ I understand you need an AI system that ingests live market data, learns from historical patterns, generates Buy/Sell/Hold signals, adapts to market regimes, and provides real-time dashboard + alerts. ⚜ Live data ingestion (APIs/CCXT) ⚜ ML models (LSTM/XGBoost) ⚜ Market regime detection logic ⚜ Real-time signal generation ⚜ Backtesting engine ⚜ Risk management module ⚜ Dashboard + alerts (Telegram/email) I have experiance in Python, data science, and building ML pipelines with real-time data. I enjoy creating systems that learns from data instead of fixed rules. My approach is modular: data pipeline → model training → regime detection → signal engine → Streamlit dashboard, with continuous retraining. Let’s connect and discuss strategy & timeline. Warm Regards, Farhin B.

@Tanmoy236

Hello! As a seasoned AI/ML engineer with over 9 years of experience building adaptive trading systems for crypto and commodities, I'll develop your self-learning signal engine that detects market regimes and generates real-time buy/sell alerts without hardcoded strategies. Here's how I can help: - Build data ingestion pipeline (Binance/Yahoo Finance/Alpha Vantage) for live OHLCV + sentiment feeds - Train LSTM/XGBoost models on historical data to auto-generate Buy/Sell/Hold signals - Implement regime detection (trending/ranging/volatile) with periodic retraining on recent data - Create Streamlit dashboard with signals, confidence scores, regime alerts + Telegram integration - Include backtesting module and risk management (stop-loss, position sizing) Quick questions: Which instruments first (Gold, BTC, others)? Also, do you have preferred intraday timeframes (1/5/15min)? Share your API keys and I'll have a prototype backtest within 1 week.

@kishanbwts

This project involves building a live AI trading signal system that learns from data without preset rules, adapts to market regimes, and works on both crypto and commodities. I’ve built similar AI-driven signal systems using real-time APIs and ML models like LSTM, designed to generate actionable intraday signals on multiple timeframes. I handled live data ingestion, regime detection, and adaptive model updates before, ensuring signals stay relevant as markets shift. Key questions: - Do you have preferred data sources/APIs for sentiment analysis, or should I suggest options? - How often do you want the model to retrain during market hours? To cover risk management and backtesting, I usually integrate stop-loss and position sizing logic into the pipeline upfront, along with a backtest module mimicking live conditions for confidence before rollout. I can start by setting up the live data feeds and basic signal engine, then build adaptive regime switching and the dashboard with alerts. Ready to get this moving as soon as you say go.

@leciffre69

⏱ Timeline: 20 days | Cost:$1200 -Experience: https://www.freelancer.com/u/leciffre69 After reviewing your description, I understood the purpose of this project is to create an AI-driven trading signal system that adapts to live market conditions, providing reliable buy/sell indicators for multiple instruments while supporting intraday trading. From my past experience, the real challenge is designing models that can learn dynamically from live market data while remaining stable under volatile conditions. This is crucial because inaccurate or delayed signals can lead to significant losses. I’ve implemented similar adaptive AI trading systems with backtesting and alert mechanisms, and I can share my previous work. To proceed, I’ll need access to historical market data, any preferred API keys, your target instruments, and confirmation on dashboard delivery requirements. This is a straightforward project for me, and I’m sure in delivering a fully functional AI trading signal system within 20 days. Let's chat now Thank you

@codefusion53

Hi, I understand you need an AI driven trading signal system that learns from live and historical market data to generate intraday buy, sell, or hold signals for assets such as Gold and BTC. The core challenge is building a model driven system that adapts to changing market conditions, supports real time operation, and remains practical through risk controls and backtesting. My approach would be to build the core engine in Python with a modular pipeline for data ingestion, feature preparation, model training, regime detection, signal generation, and alerting. I would use a learning based model such as XGBoost or LSTM, then add market condition classification to distinguish trending, ranging, and volatile periods so signal behavior can adjust accordingly. I would also include a simple dashboard showing signal, confidence, and regime, plus Telegram or email alerts. A backtesting layer and risk module for stop loss and position sizing will be included so the system can be validated before any live use. Before delivery, I will test data quality, model stability, signal timing, backtesting accuracy, alert delivery, and regime switching logic to ensure the system performs reliably under real intraday conditions. Best, Justin

@ashinnd84

Hello there, we are a team of developers and we can develop your auto trading scalable application. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@quan9824

Hi, this is exactly the kind of end-to-end trading system I build—data pipeline, ML signal engine, and real-time deployment. My approach is to design a modular, adaptive system rather than a single static model: 1. Data Layer I’ll build a reliable ingestion pipeline using CCXT and market APIs to stream OHLCV data (multi-timeframe) into PostgreSQL, with optional sentiment integration. 2. Signal Engine (AI-first) Instead of hardcoded rules, I’ll train models (XGBoost + LSTM hybrid if needed) to learn patterns directly from data. Features will include price structure, volatility, and momentum. Output: Buy/Sell/Hold + confidence score. 3. Market Regime Detection I’ll implement a regime classifier (trend/range/volatility) that dynamically adjusts model behavior or switches sub-models for better intraday performance. 4. Risk & Backtesting A full backtesting framework will validate performance before live use, including stop-loss logic, position sizing, and drawdown control. 5. Real-Time System + UI Live signal generation (1m–15m) with a Streamlit dashboard and alerts via Telegram/email. Optimized for low latency and stability. Deliverables • Data pipeline + database schema • Trained models + retraining pipeline • Backtesting + evaluation reports • Live signal engine + dashboard • Clean, documented codebase I focus on building systems that are robust, testable, and actually usable in live environments—not just experimental models.

@juanjosec63

Hi, Over 9 years experience in (Python, AI/ML, TensorFlow, PyTorch, scikit-learn, CCXT, Streamlit, PostgreSQL, and real-time market data systems). For this project, I am going to build an AI-based trading signal system that ingests live OHLCV data, trains models like LSTM or XGBoost on historical market behavior, detects changing market regimes such as trending or volatile conditions, and delivers real-time buy, sell, or hold signals with confidence scoring, backtesting, risk controls, and alert delivery through Telegram or email in a simple dashboard. You can expect clear communication, fast turnaround, and a high-quality result. Best regards, Juan

@mehranriaz0

Greetings, I'm excited about your project to develop an AI-based auto trading signal system. From what you've described, you're looking to create a smart system that generates real-time buy and sell signals for trading instruments like Gold and Bitcoin, adapting to market conditions without relying on hardcoded strategies. To tackle this, I would focus on building robust ML models, such as LSTM or XGBoost, that learn from historical data. Integrating live price feeds and potentially sentiment analysis will enhance the system's responsiveness. I’d also ensure that the system can detect market regimes and adjust its strategies accordingly. Having a solid risk management module will be crucial for safe trading. What specific market conditions or events do you think are most critical for the signal engine to adapt to? Best regards, Mehran Riaz

@mykhaylob4

✋ Hi there. I can build your AI based trading signal system that generates real time buy and sell signals using live market data, machine learning models, and adaptive market detection for intraday trading. ✔️ I have solid experience building Python based data systems with machine learning models for time series forecasting and financial data processing. In a previous project, I worked on a trading analytics system using LSTM and XGBoost models with live API feeds, backtesting modules, and alert based signal outputs. ✔️ For your project, I will build a full AI pipeline in Python that ingests live OHLCV data from sources like Binance or Alpha Vantage, processes it, and feeds it into trained ML models to generate Buy, Sell, or Hold signals with confidence scoring. ✔️ I will implement market condition detection to identify trending, ranging, or volatile phases and adjust model behavior based on real time conditions for better accuracy. ✔️ I will also add a backtesting module so you can test strategies on historical data, along with risk management logic for stop loss and position sizing suggestions. ✔️ I will create a simple Streamlit dashboard showing live signals, market state, and alerts via Telegram or email for real time updates. Let’s chat to review your requirements and start. Best regards, Mykhaylo