<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Haidar S. Ali</title>
    <description>The latest articles on DEV Community by Haidar S. Ali (@haidar_ali0).</description>
    <link>https://dev.to/haidar_ali0</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4122430%2Fe203b8c7-2f04-47da-a6eb-8fe2e9605f6c.jpg</url>
      <title>DEV Community: Haidar S. Ali</title>
      <link>https://dev.to/haidar_ali0</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/haidar_ali0"/>
    <language>en</language>
    <item>
      <title>Open Source 𝐓𝐫𝐚𝐝𝐢𝐧𝐠 𝐒𝐢𝐠𝐧𝐚𝐥 𝐒𝐞𝐫𝐯𝐞𝐫</title>
      <dc:creator>Haidar S. Ali</dc:creator>
      <pubDate>Sun, 13 Sep 2026 00:59:54 +0000</pubDate>
      <link>https://dev.to/haidar_ali0/open-source-1fb6</link>
      <guid>https://dev.to/haidar_ali0/open-source-1fb6</guid>
      <description>&lt;p&gt;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.&lt;br&gt;
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.&lt;br&gt;
For this reason, I built a system that prepares and delivers all required market data to LLMs in a clean, structured, and consistent format.&lt;br&gt;
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.&lt;br&gt;
𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀&lt;br&gt;
• 𝗕𝗶𝗻𝗮𝗻𝗰𝗲 𝗺𝗮𝗿𝗸𝗲𝘁 𝗱𝗮𝘁𝗮 including candles, order‑book metrics, recent trades, and technical indicators&lt;br&gt;
• 𝗪𝗲𝗯‑𝘀𝗲𝗮𝗿𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 for crypto news, whale activity, policy, macro events, exchange updates, and whale alerts&lt;br&gt;
• 𝗢𝗽𝗲𝗻𝗥𝗼𝘂𝘁𝗲𝗿 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 for choosing free or paid models from OpenAI, Google, Anthropic, Mistral, and others&lt;br&gt;
• 𝗟𝗟𝗠 𝘃𝗼𝘁𝗶𝗻𝗴 and repeated iterations for consistency checks&lt;br&gt;
• 𝗣𝗿𝗼𝗺𝗽𝘁 𝗳𝗶𝗹𝗲𝘀 mapped to models and iteration cycles&lt;br&gt;
• 𝗤𝘂𝗮𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 for additional market evidence, with selected model families running in parallel&lt;br&gt;
• 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗯𝗮𝗰𝗸𝘁𝗲𝘀𝘁𝗶𝗻𝗴 before live signal analysis&lt;br&gt;
• 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 including win rate, returns, drawdown, profit factor, buy‑and‑hold comparison, outperformance, LLM agreement, direction accuracy, confidence calibration, and cost&lt;br&gt;
• 𝗧𝗲𝗹𝗲𝗴𝗿𝗮𝗺 𝗮𝗹𝗲𝗿𝘁𝘀 for qualifying signals&lt;br&gt;
• 𝗦𝗮𝘃𝗲𝗱 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀 and prompt files for reproducible experiments&lt;br&gt;
• 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&lt;br&gt;
• 𝗟𝗼𝗰𝗮𝗹 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 for live signal analysis, testing, configurations, prompts, settings, logs, and ML data&lt;/p&gt;

&lt;p&gt;The platform is designed for continuous research, experimentation, and strategy improvement. It generates signals and alerts but does not place exchange orders automatically.&lt;br&gt;
🔗 Project: &lt;a href="https://github.com/haidarali0/Trading-Signal-Server" rel="noopener noreferrer"&gt;https://github.com/haidarali0/Trading-Signal-Server&lt;/a&gt;&lt;/p&gt;

</description>
      <category>ai</category>
      <category>llm</category>
      <category>cryptocurrency</category>
      <category>machinelearning</category>
    </item>
  </channel>
</rss>
