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    <title>DEV Community: devamahesh198</title>
    <description>The latest articles on DEV Community by devamahesh198 (@devamahesh198).</description>
    <link>https://dev.to/devamahesh198</link>
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      <title>DEV Community: devamahesh198</title>
      <link>https://dev.to/devamahesh198</link>
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    <item>
      <title>AI financial analyst</title>
      <dc:creator>devamahesh198</dc:creator>
      <pubDate>Wed, 28 Oct 2026 23:19:11 +0000</pubDate>
      <link>https://dev.to/devamahesh198/ai-financial-analyst-3m46</link>
      <guid>https://dev.to/devamahesh198/ai-financial-analyst-3m46</guid>
      <description></description>
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    <item>
      <title>Building an Autonomous AI Financial Analyst with Python and Tool Orchestration</title>
      <dc:creator>devamahesh198</dc:creator>
      <pubDate>Wed, 28 Oct 2026 23:13:17 +0000</pubDate>
      <link>https://dev.to/devamahesh198/building-an-autonomous-ai-financial-analyst-with-python-and-tool-orchestration-30pa</link>
      <guid>https://dev.to/devamahesh198/building-an-autonomous-ai-financial-analyst-with-python-and-tool-orchestration-30pa</guid>
      <description>&lt;p&gt;Hey everyone! It's my first post, so this is fun. I've been building a pipeline for a project I really want to start. On the surface, it's basically a Financial Analyst for myself. What I am really trying to do is push it to be as accurate as it can be through systems development. So this is the current pipeline by far:&lt;/p&gt;

&lt;p&gt;Input the top 50 stocks of the S&amp;amp;P -&amp;gt;&lt;/p&gt;

&lt;p&gt;Filter stocks ( process still in design ) -&amp;gt;&lt;/p&gt;

&lt;p&gt;Pass all filtered stocks (if any) to Haiku -&amp;gt;&lt;/p&gt;

&lt;p&gt;Filter again using qualitative data such as news and market trends -&amp;gt;&lt;/p&gt;

&lt;p&gt;Stock data can be discarded, saved in a JSON file as potentially useful, or passed along -&amp;gt;&lt;/p&gt;

&lt;p&gt;Pass all filtered stocks and qualitative data (if any) to a more powerful model (Sonnet or higher) -&amp;gt;&lt;/p&gt;

&lt;p&gt;Use all provided data, including pattern records from the backtesting stage, to output a clear decision with a reason -&amp;gt;&lt;/p&gt;

&lt;p&gt;Pass the output to another model for critiquing. If the critique would be minor, skip. Otherwise, return the critique to the previous model to revise and repeat -&amp;gt;&lt;/p&gt;

&lt;p&gt;Replicate the decision in paper trading during backtesting to record performance results -&amp;gt;&lt;/p&gt;

&lt;p&gt;Pass the results to an AI model to output an explanation for why this happened, along with a confidence rating (which can also be passed back into the model for a quick critique pass) -&amp;gt;&lt;/p&gt;

&lt;p&gt;Record the explanation and result permanently in a JSON file -&amp;gt;&lt;/p&gt;

&lt;p&gt;If the same result occurs several times and the explanation is similar, pass the data back to Sonnet to modify the core strategy.&lt;/p&gt;

&lt;p&gt;I'm also planning to add a backtesting mechanic that uses all of the data to search for correlations between two variables, test them across several historical periods, log them if they're consistent, and forward-test them in the current market until they're fully validated as a usable pattern for the current model.&lt;/p&gt;

&lt;p&gt;Can you list all of your suggestions and critiques? Try being helpful, not rude, etc.&lt;/p&gt;

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      <category>ai</category>
      <category>python</category>
      <category>buildinpublic</category>
      <category>architecture</category>
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