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Infinite Bagholder
Pluggable, local-first developer harness for agentic trading
Infinite Bagholder is an open-source, self-hosted Python framework for LLM-powered stock trading on Alpaca and similar brokerages. It provides a local web dashboard to monitor execution logs and agent reasoning in real time. Rather than relying on prompts to enforce risk boundaries, it runs programmatic Python guardrails (like stop-losses and allocation limits) locally before orders are dispatched. Includes a pluggable system for custom news feeds, brokerages, and tools.
I’m Ricky, the developer behind Infinite Bagholder.
Let’s be honest: discerning the signal-to-noise ratio of the current discourse around autonomous trading agents is an exercise in futility. You’ve probably seen the pitches promising hands-off wealth generation via API keys and system prompts. In practice, putting an LLM in direct control of an active trading account without a leash is a recipe for a rapid margin call. Models hallucinate, APIs drop requests, and LLMs are notoriously bad at basic math.
I built Infinite Bagholder because I thought the world needed a local sandbox that handles the administrative plumbing (event loops, state management, and logging) while keeping the model under strict, programmatic constraints.
It is a self-hosted Python framework built on FastAPI and SQLite (WAL). It shifts the focus of algorithmic trading away from complex codebase maintenance and toward clear logic. By expressing trading strategies in "plain English" Markdown, developers and quantitative traders can write, run, and test logic without building database tables or handling API loops.
Key Features:
User-Configurable Safety Envelopes: Orders are validated against local Python limits (specified through user settings) before they are ever dispatched to the Alpaca API. If the model hallucinates an invalid symbol or size, the local framework rejects it immediately.
Closed-Loop Strategy Evolution: Includes a dedicated evolution module. You can feed past session transcripts, transaction history, and net P&L/API costs back into a secondary LLM. The system analyzes its own execution history, generates a written critique, and automatically refines the Markdown strategy prompt to optimize performance over time.
Quantitative Inputs: Rather than asking the LLM to calculate indicators, a local technical engine computes Relative Volume (RVOL), SMAs, and Volume Z-Scores and feeds them to the model as structured parameters.
Modular Plugin Interface: Extensible via abstract base classes, allowing you to drop in custom news parsers (e.g. SEC filings), alternate brokerages, or custom tools without touching the core code. Simulated Execution: A built-in mock broker lets you dry-run strategies offline to inspect the model's decision logs before risking paper or live capital.
Comprehensive Documentation: The repository includes a fully-featured documentation site built by Mkdocs and hosted by Github pages. Aside from a comprehensive codebase reference, this documentation includes ongoing case studies in agent psychology, prompt engineering, and other goodies.
The codebase is open source under the GNU AGPLv3. I’d love to hear your thoughts on the architectural implementation or how you are structuring local agent workflows. I’ll be in the comments to answer questions!
About Infinite Bagholder on Product Hunt
“Pluggable, local-first developer harness for agentic trading”
Infinite Bagholder was submitted on Product Hunt and earned 0 upvotes and 3 comments, placing #143 on the daily leaderboard. Infinite Bagholder is an open-source, self-hosted Python framework for LLM-powered stock trading on Alpaca and similar brokerages. It provides a local web dashboard to monitor execution logs and agent reasoning in real time. Rather than relying on prompts to enforce risk boundaries, it runs programmatic Python guardrails (like stop-losses and allocation limits) locally before orders are dispatched. Includes a pluggable system for custom news feeds, brokerages, and tools.
On the analytics side, Infinite Bagholder competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Infinite Bagholder performed against the three products that launched closest to it on the same day.
Who hunted Infinite Bagholder?
Infinite Bagholder was hunted by Ricky Harlow. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Infinite Bagholder including community comment highlights and product details, visit the product overview.
Ricky Harlow
Hi Product Hunt,
I’m Ricky, the developer behind Infinite Bagholder.
Let’s be honest: discerning the signal-to-noise ratio of the current discourse around autonomous trading agents is an exercise in futility. You’ve probably seen the pitches promising hands-off wealth generation via API keys and system prompts. In practice, putting an LLM in direct control of an active trading account without a leash is a recipe for a rapid margin call. Models hallucinate, APIs drop requests, and LLMs are notoriously bad at basic math.
I built Infinite Bagholder because I thought the world needed a local sandbox that handles the administrative plumbing (event loops, state management, and logging) while keeping the model under strict, programmatic constraints.
It is a self-hosted Python framework built on FastAPI and SQLite (WAL). It shifts the focus of algorithmic trading away from complex codebase maintenance and toward clear logic. By expressing trading strategies in "plain English" Markdown, developers and quantitative traders can write, run, and test logic without building database tables or handling API loops.
Key Features:
User-Configurable Safety Envelopes: Orders are validated against local Python limits (specified through user settings) before they are ever dispatched to the Alpaca API. If the model hallucinates an invalid symbol or size, the local framework rejects it immediately.
Closed-Loop Strategy Evolution: Includes a dedicated evolution module. You can feed past session transcripts, transaction history, and net P&L/API costs back into a secondary LLM. The system analyzes its own execution history, generates a written critique, and automatically refines the Markdown strategy prompt to optimize performance over time.
Quantitative Inputs: Rather than asking the LLM to calculate indicators, a local technical engine computes Relative Volume (RVOL), SMAs, and Volume Z-Scores and feeds them to the model as structured parameters.
Modular Plugin Interface: Extensible via abstract base classes, allowing you to drop in custom news parsers (e.g. SEC filings), alternate brokerages, or custom tools without touching the core code.
Simulated Execution: A built-in mock broker lets you dry-run strategies offline to inspect the model's decision logs before risking paper or live capital.
Comprehensive Documentation: The repository includes a fully-featured documentation site built by Mkdocs and hosted by Github pages. Aside from a comprehensive codebase reference, this documentation includes ongoing case studies in agent psychology, prompt engineering, and other goodies.
The codebase is open source under the GNU AGPLv3. I’d love to hear your thoughts on the architectural implementation or how you are structuring local agent workflows. I’ll be in the comments to answer questions!