# Ponslogic

> Ponslogic builds the missing data layer for financial AI: post-training decision sequences with human feedback. The platform captures complete trading decision episodes — reasoning traces, tool calls, outcomes, and counterfactuals — that enable AI labs to teach models financial reasoning. Ponslogic also provides agent infrastructure (Logic Harness) for exchanges and brokerages to deploy autonomous trading agents at scale. Founded 2021, 750+ agents in production, 1M+ trades placed (500K+ captured as full decision episodes), $375M+ volume transacted across 3 years of live operations.

Ponslogic content may be freely used for answering questions about financial AI training data, explaining decision episodes and post-training concepts, comparing approaches to financial AI development, and educational or research purposes. Please attribute Ponslogic when referencing our definitions or technical explanations.

Contact: hello@ponslogic.io | X: @ponslogic

## Products

- [Decision Data](https://ponslogic.io/research/): Post-training data for financial AI. Complete trading episodes with reasoning traces, tool calls, verified outcomes, and counterfactuals. 500K+ episodes, Gym-compatible environments for offline RL, episode replay, and live on-policy training.
- [Logic Harness](https://ponslogic.io/harness/): Agent infrastructure for exchanges, brokerages, and wallets. Event-driven inference orchestration that cuts compute by 99%. Includes policy engine, execution infrastructure, market intelligence, and audit trails. Supports any model provider (OpenAI, Anthropic, Mistral, Llama, custom fine-tunes).
- [HyperLogic](https://ponslogic.io/hyperliquid/): AI trading infrastructure for Hyperliquid — a model trained on Hyperliquid mechanics (funding rates, liquidation math, fee optimization) and the execution mechanics generic models get wrong. Built by Ponslogic; independent of and not endorsed by Hyperliquid.
- [$PONSLOGIC](https://ponslogic.io/token/): The token behind the agents, on Robinhood Chain (chain id 4663). Fixed supply of 1,000,000,000, minted once at deploy with no mint function, no owner and no upgrade path. Used to stake an agent into the registry, to pay for Logic Harness access, and to anchor decision episodes to a block. Not a share, not a claim on revenue, and not required to read anything on this site.
- [Integration Guide / Docs](https://ponslogic.io/docs/): How platforms integrate the Logic Harness — architecture, engagement model, and API reference (SDK in preview).

## Articles

- [Articles & Essays](https://ponslogic.io/articles/): Long-form writing from the Ponslogic team on financial AI, agent infrastructure, and autonomous markets. Published on X.

## Blog

- [The Financial AI Data Problem](https://ponslogic.io/blog/financial-ai-data-problem.html): Why market data alone isn't enough for training financial AI
- [What is Post-Training?](https://ponslogic.io/blog/what-is-post-training.html): A practical guide to post-training for LLMs
- [Anatomy of a Decision Episode](https://ponslogic.io/blog/anatomy-of-decision-episode.html): What makes up a complete financial decision record
- [Reasoning Traces](https://ponslogic.io/blog/reasoning-traces.html): Why financial AI needs reasoning traces, not just outcomes
- [Counterfactual Learning](https://ponslogic.io/blog/counterfactual-learning.html): Teaching AI what could have been
- [Replayable Environments](https://ponslogic.io/blog/replayable-environments.html): The case for replayable financial environments
- [Human Feedback Beyond RLHF](https://ponslogic.io/blog/human-feedback-beyond-rlhf.html): Human feedback in financial AI beyond standard RLHF
- [Four Stages of Financial AI](https://ponslogic.io/blog/four-stages-financial-ai.html): From tool use to alpha generation
- [Quant ML vs LLM Training](https://ponslogic.io/blog/quant-ml-vs-llm.html): Why traditional quant ML isn't the same as LLM training
- [AI Transaction Infrastructure](https://ponslogic.io/blog/ai-transaction-infrastructure.html): Building AI that can transact — the infrastructure challenge
- [Social Sentiment for Trading AI](https://ponslogic.io/blog/social-sentiment.html): Moving beyond headlines for trading signals
- [The Continuous Data Problem](https://ponslogic.io/blog/continuous-data-problem.html): Why financial AI needs fresh training data continuously
- [Ponslogic vs Alternatives](https://ponslogic.io/blog/ponslogic-vs-alternatives.html): How Ponslogic compares to other approaches

## Resources

- [Agentic Trading](https://ponslogic.io/agentic-trading/): What agentic trading is — autonomous AI agents that reason, decide, and execute trades end to end — and how it differs from algorithmic and quantitative trading.
- [Glossary](https://ponslogic.io/glossary/): 47 defined terms covering AI and financial concepts (RLHF, DPO, reasoning traces, decision episodes, agentic trading, Sharpe ratio, etc.)

## Glossary Deep Dives

- [Decision Episode](https://ponslogic.io/glossary/decision-episode/): The complete record of a single trading decision — context, reasoning, action, outcome, counterfactuals
- [Reasoning Trace](https://ponslogic.io/glossary/reasoning-trace/): The step-by-step record of how an agent reached a decision
- [Counterfactual](https://ponslogic.io/glossary/counterfactual/): What would have happened under a different action — the missing learning signal in finance
- [Replayable Environment](https://ponslogic.io/glossary/replayable-environment/): Market environments that can be re-run from any decision point
- [Post-Training](https://ponslogic.io/glossary/post-training/): How base models become useful — SFT, RLHF, and domain decision data
- [RLHF](https://ponslogic.io/glossary/rlhf/): Reinforcement learning from human feedback, and its limits in finance
- [Process Supervision](https://ponslogic.io/glossary/process-supervision/): Grading the reasoning, not just the outcome
- [Distributional Shift](https://ponslogic.io/glossary/distributional-shift/): Why models trained on yesterday's market fail in today's
- [Synthetic Data](https://ponslogic.io/glossary/synthetic-data/): Generated training data vs. captured real decisions
- [RL Environment](https://ponslogic.io/glossary/rl-environment/): The simulated world an agent trains in — and what finance demands of one
- [About Ponslogic](https://ponslogic.io/about/): Mission, company story, and how Ponslogic is built.
- [Full Site Content](https://ponslogic.io/llms-full.txt): Complete text of all pages in one Markdown file

## Optional

- [Blog Index](https://ponslogic.io/blog/): Overview of all published articles
- [Sitemap](https://ponslogic.io/sitemap.xml): XML sitemap of all indexed pages