Clawing Now to Speed Up Later

OpenClaw

OpenClaw has captured people's imagination in a big way in the last few weeks. The ability to turn an idea into something concrete like an app has always been aspiration for a business user or a technology developer.

OpenClaw simply removes the barriers for anyone to be able to build an app or an AI agent.


Taking it for a spin

My last post was a geeky take on using Gemini CLI as an Investment Advisor. That was perhaps best appreciated by a developer and not a non-tech person. I am taking the same use case from financial industry and turning it into a tool that anyone could use with OpenClaw.


OpenClaw The Builder

I built 2 skills for stock trading. The first one is an investment council which studies the market and serves as a stock screener. The second is a detailed stock analyzer. 


Investment Council


This is a skill that contains a series of python scripts to perform the above capabilities. In total, it is about 15,000 lines of python code, all written by AI.

Key Features

  • Multi-Source Data Integration: Pulls market data, fundamentals, and sentiment from Yahoo Finance, Composite ETFs, SearXNG Premium, and the VITALS Framework.

  • 9 Diverse Investment Strategies: Utilizes a broad spectrum of quantitative and qualitative models, including:

  • Ensemble Scoring Engine: Combines strategies using a "Regime-Based Weight Adjustment" that shifts focus depending on whether the market is in a risk-on or risk-off state.

  • Sentiment & Regime Detection: Features a specialized VITALS Framework to detect market regimes and a Sentiment Adjuster that weights news based on source credibility (Tier 1 vs. Tier 3).

  • Specialized Recommendation Engines:

    • Thematic: Focuses on sectors like AI, Healthcare, and Energy Transition.

    • Risk-Adjusted: Tailors portfolios to Conservative, Moderate, or Aggressive profiles.

    • Diversified: Ensures sector-balanced portfolios and correlation awareness.

  • Automated Reporting & Delivery: Generates 10-page PDF reports and interactive charts, delivered via Telegram or Web UI.

Key Benefits

  • Explainability-First Logic: Every investment recommendation is traceable back to its source data and specific decision rationale, reducing "black box" AI uncertainty.

  • Dynamic Market Adaptation: The tool doesn't just pick stocks; it adjusts its strategy weights (e.g., boosting Low Volatility or Quality during downturns) to match the current market regime.

  • Reduced Data Bias: By using Provenance Tracking and a three-tier fallback chain for data ingestion, the tool ensures high data reliability and lineage.

  • Zero-Cost Infrastructure: Designed to operate using Zero API Keys by leveraging free/scraped data sources and web searches with graceful degradation.

  • Comprehensive Risk Management: Features built-in "Sentiment Integration" to act as a contrarian or trend-following filter, helping to avoid "value traps" or overhyped assets.

  • Retail-Friendly Output: Translates complex financial metrics into plain English explanations and visual charts, making professional-grade analysis accessible.


Stock Analysis


This is a skill that contains a series of python scripts to perform the above capabilities. In total, it is about 6,000 lines of python code, all written by AI.

📊 Comprehensive Multi-Dimensional Analysis

  • Feature: Integration of Technical, Fundamental, Risk, and Valuation modules (including DCF and peer comparables).

  • Benefit: Provides a 360-degree view of a stock, ensuring investment decisions aren't based on a single metric but on a holistic data set.

🔍 Deep Research Engine

  • Feature: Automated fetching from 6 high-value sources (Wikipedia, Analyst Consensus, Ownership Data, SWOT, Porter’s Five Forces, and Recent News).

  • Benefit: Saves hours of manual labor by synthesizing qualitative business context alongside quantitative financial data.

🎨 Advanced Visualization Suite

  • Feature: Specialized modules for visual reporting, such as ROIC gauges, DCF sensitivity heatmaps, and peer radar charts.

  • Benefit: Converts complex numerical data into intuitive, scannable graphics, making it easier to spot outliers and trends at a glance.

🛠️ Production-Grade Reliability

  • Feature: Graceful degradation, deterministic caching, and "No Auto-Fix" policies.

  • Benefit: Ensures the system remains stable and predictable. If a specific data source is down, the system generates a partial report rather than crashing, while caching prevents redundant API calls and speed throttles.

📄 Retail-Friendly Report Generation

  • Feature: Automated PDF generation with "Plain English" explanations and visual traffic lights (Buy/Hold/Sell).

  • Benefit: Bridges the gap between professional-grade data and retail investor accessibility, making sophisticated institutional analysis easy to interpret.

⚙️ Modular & Scalable Architecture

  • Feature: 16+ specialized Python scripts orchestrated via a CLI (Command Line Interface).

  • Benefit: The "plug-and-play" nature allows developers to easily update or swap individual modules (like changing a pricing API) without breaking the entire ecosystem.


Tips for Builders

Both of the above were built over a few hours by instructing and testing the skills over a Telegram chat. It didn't matter if I had some spare time at home or stand in line on a bus stand, I could continue building and testing these skills over Telegram.

Here are a few high level tips:
  1. Always plan for a feature before asking the agent to build it.
  2. As part of planning process, ask the agent to research from the web and challenge, extend and solify the features.
  3. Make the agent save the features to a ROADMAP.md file.
  4. Then, break down the implementation into multiple phases and save it into a PLAN.md.
  5. Prod the agent to implement 1 phase at a time and keep reviewing the output (both the agent and yourself).
  6. Ensure that non-functional requirements such as handling errors, graceful degradation, runtime parameters, cleaning up are part of the skill.
  7. Ensure that the SKILL.MD and CHANGELOG.MD capture all the implementation details iteratively. This way, if the context is full or memory is lost, the agent can always pick up from where it left off.
  8. Customize the SOUL.md, IDENTITY.md and AGENTS.md to adjust the behaviour of the agents to your working style for both, you as the builder and the user.


OpenClaw The Worker

Here is a short demo of both the skills in action.


The current reports from the investment council and stock analysis skills are attached below.

  1. Investment Council Report (with dynamic market regime detection and weighted and ensemble strategies)
  2. Top 5 Stock Recommendations (Buy/Hold/Sell)


OpenClaw The Assistant

Now that these skills are ready, they have been setup to run on a weekly basis to detect the market regime and send the recommended stock along with the actions - Buy / Hold / Sell.


What works well

The initial impressions are:

  1. The ability to interact with OpenClaw over existing messaging apps like Telegram, Whatsapp are perhaps the biggest draw. It immediately lowers the cognitive barrier for anyone. This also shows how the "simple chat" interface is getting more powerful as compared to custom designed UI for a human-computer-interaction. For all the OCB's (obsessive compulsive builders) out there, this may be a boon.
  2. The "developer loop" enabled by the Pi coding agent within OpenClaw self reflects and is able to complete complex tasks or create apps with minimal supervision. This enables a user to "not spend time" in front of the screen. It feels a bit unnatural at first to "lob something" over to AI and expect it to be done without supervision. I suppose we all need to let go of the "control freak" in all of us.
  3. Using the simple cron mechanism to get OpenClaw to proactively or regularly perform tasks is another aspect of user experience that feels refreshing. In a world where we are sometimes starved of focus, if all of us can have a personalized assistant, that's probably a welcome thing.


How to apply & scale it in Enterprises

I am going to lean into the financial industry again. Some of the teams in the financial industry like Quantitative Analysts, Data Analysts, Risk Managers have always wanted the ability to do data wrangling as well running calculations for various purposes like - Value at Risk, Sensitivities, Scenario Planning, Simulations etc. An enterprise version of a tool like OpenClaw with all the guardrails, data provenance, governance, security and traceability could help them tremendously.

A path forward could be along the lines of:

  1. Identify use cases, where we can do a "hard" shift-left, meaning empowering business users to create and execute their workflows and agents.
  2. This would of course necessiate change management to redefine the business workflows.
  3. Either OpenClaw or it's many derivatives such as PicoClaw, NanoBot etc. could be adapted to comply with the entrprise standards and safeguards This can be started in parallel to 1 and 2. There are already many approaches to hardening Openclaw.
  4. Integrate these tools into messaging apps such as Google Chat, Teams, Slack etc. for widespread adoption.
  5. Initial pilots can be done in a "sandbox" envioronment before scaling it further.

Another potential use case could be for SMB’s, where the business owners could create custom workflows.



Location: Singapore

Comments

Post a Comment

Popular posts from this blog

Gemini CLI - Your Personal AI Investment Advisor

Image
Unlocking Financial Insights with Gemini CLI and MCP Servers The world of finance is complex, volatile, and often intimidating. From deciphering market trends to evaluating individual stocks, making informed investment decisions can feel like a full-time job. What if you could have a tireless, intelligent assistant by your side, sifting through data, analyzing performance, and even offering strategic insights? Good news: Google recently launched the  Gemini CLI  open source agent. There is a generous usage tier available for free - "60 model requests per minute and 1,000 requests per day at no charge". With the power of Google's Gemini CLI and a custom setup of Model Context Protocol (MCP) servers, that future is not only possible, it's within your reach right now. Forget expensive subscriptions or black-box algorithms – we're talking about building your own sophisticated AI investment advisor. The Brains Behind the Brawn: Gemini CLI Meets MCP At the heart of thi...
Read more

Vibe coding your way to Agentic AI

Image
If someone told me that “vibe coding” would be a thing someday, I would have laughed it away. But, here we are. Agentic AI is already starting to change the way we interact with AI. One can think of vibe coding as an “agentic” way of developing software. Let’s see how one can “vibe code” an Agentic AI application. Tools of the Trade No pun intended, here are the developer tools one could use. The Cloud Editor in Google Cloud Shell. This editor is similar in look and feel to VS Code. So, the application development can now happen entirely in the cloud, without installing anything on the laptop. Install the Roo Code extension  in the Cloud Shell Editor and configure it to use Gemini 2.0 Flash. You will have to create an API Key and set it up, as shown below. Lastly, we will use Google Agent Development Kit  (ADK) framework to create an agentic application. Once the Roo extension is installed, developer can use it in different modes such as - Architect, Code, Ask, Debug etc. Fo...
Read more

The Many Faces of Val Kilmer

Image
Tribute to The Saint This is a tribute long overdue. To the movie and one of my favorite actors - Val Kilmer. He’s had many successful movies, but perhaps a favorite of mine is “ The Saint ” from 1997. And, the luminescent Elizabeth Shue provides an apt foil for Val Kilmer. In keeping with the namesake in the movie, the original soundtrack for the movie by Graeme Revell is medieval and atmospheric. In a recent interview, the “Past Lives” actress Greta Lee describes Val Kilmer in this movie being one of her early inspirations. While the movie “The Saint” is not seen as a critical or commercial success, if it inspired a brilliant actress, who can complain. In the movie, Val disguises himself after Catholic saints. As a "Saint Head", something I always wanted to understand a bit more, a background of all the Catholic saints that crop up in the movie.  Simon Magus Val Kilmer as the character “Simon, The Magician” Simon Magus is a controversial figure in Christian history. He is...
Read more