Open Source 𝐓𝐫𝐚𝐝𝐢𝐧𝐠 𝐒𝐢𝐠𝐧𝐚𝐥 𝐒𝐞𝐫𝐯𝐞𝐫

A developer built the Trading Signal Server, an open-source system that feeds structured crypto market data — Binance candles, order-book metrics, trades, technical indicators, and web-search context — into LLMs for signal research and evaluation. The platform supports OpenRouter model selection across OpenAI, Google, Anthropic, and Mistral, LLM voting, quantitative models, historical backtesting, performance metrics, and Telegram alerts, while automatically labeling saved signals with future candles for ML training. It generates signals but does not place exchange orders automatically.

LLMs can become useful analysis assistants when they have structured data and real‑time market context, meaning they have enough relevant information for the task. Getting specific indicators, news, and candles into an LLM is difficult, even with search tools integrated. You still need structured inputs, the right context, and a way to ensure everything the model receives is accurate and relevant. For this reason, I built a system that prepares and delivers all required market data to LLMs in a clean, structured, and consistent format. I'm excited to share my 𝐓𝐫𝐚𝐝𝐢𝐧𝐠 𝐒𝐢𝐠𝐧𝐚𝐥 𝐒𝐞𝐫𝐯𝐞𝐫, a research‑driven system for developing and evaluating crypto signals with LLMs, quantitative models, and historical data. 𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 • 𝗕𝗶𝗻𝗮𝗻𝗰𝗲 𝗺𝗮𝗿𝗸𝗲𝘁 𝗱𝗮𝘁𝗮 including candles, order‑book metrics, recent trades, and technical indicators • 𝗪𝗲𝗯‑𝘀𝗲𝗮𝗿𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 for crypto news, whale activity, policy, macro events, exchange updates, and whale alerts • 𝗢𝗽𝗲𝗻𝗥𝗼𝘂𝘁𝗲𝗿 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 for choosing free or paid models from OpenAI, Google, Anthropic, Mistral, and others • 𝗟𝗟𝗠 𝘃𝗼𝘁𝗶𝗻𝗴 and repeated iterations for consistency checks • 𝗣𝗿𝗼𝗺𝗽𝘁 𝗳𝗶𝗹𝗲𝘀 mapped to models and iteration cycles • 𝗤𝘂𝗮𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 for additional market evidence, with selected model families running in parallel • 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗯𝗮𝗰𝗸𝘁𝗲𝘀𝘁𝗶𝗻𝗴 before live signal analysis • 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 including win rate, returns, drawdown, profit factor, buy‑and‑hold comparison, outperformance, LLM agreement, direction accuracy, confidence calibration, and cost • 𝗧𝗲𝗹𝗲𝗴𝗿𝗮𝗺 𝗮𝗹𝗲𝗿𝘁𝘀 for qualifying signals • 𝗦𝗮𝘃𝗲𝗱 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀 and prompt files for reproducible experiments • 𝗦𝗲𝗹𝗳‑𝗹𝗮𝗯𝗲𝗹𝗶𝗻𝗴 𝗠𝗟 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 signals are saved automatically, and the system later checks real future candles to label each one, giving you clean ground‑truth data for future agent training • 𝗟𝗼𝗰𝗮𝗹 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 for live signal analysis, testing, configurations, prompts, settings, logs, and ML data The platform is designed for continuous research, experimentation, and strategy improvement. It generates signals and alerts but does not place exchange orders automatically. 🔗 Project: https://github.com/haidarali0/Trading-Signal-Server https://github.com/haidarali0/Trading-Signal-Server