Why We Retired the Trading Bot
We retired our paper trading bot to focus on governed AI operations. Here is what we can and cannot say about the run, and why we stopped.
Build-in-public moment: This is the full, unfiltered story. No spin. If you followed our paper trading log, you deserve an honest postmortem.
The full numbers are in the retired-project case study.
What We Built
We built an experimental algorithmic trading bot for crypto and prediction markets and ran it as simulated paper trading. Its design included a critic agent that reviewed trades before execution and could veto them if risk parameters were breached, circuit breakers that paused trading after drawdown thresholds, and Telegram alerts. The design is described in our architecture and risk posts.
We never went live with real capital. That was intentional: the bot was always a paper trading validation, because we wanted to test it in simulation before committing any money.
What We Can Say About the Results
Paper trading is a simulation: it does not capture real slippage, regime changes, or live execution. We originally withheld performance figures because we had not tied them to a retained run record. We have since reviewed the trade ledger and decision logs that survived on our own machines. They show a simulated run from February 26 to April 8, 2026 that ended net negative: both paper books finished below their $10,000 starting balance (crypto $9,341.28, Polymarket $9,467.27), and the 821 closed trades we could recover and de-duplicate sum to -$443.11 in recorded P&L, with 94 winners. The full numbers, method, and caveats are in the retired-project case study. This is simulated paper trading, not live results, and not financial advice.
A simulated run that ends net negative is not evidence of an edge, and we did not treat it as one.
The Real Reason We Stopped
The run ended net negative, and we are not presenting it as anything else. The reason we retired the bot was a focus decision.
Trading is a full-time discipline. Markets are always on, and strategies can stop working. Staying competitive requires continuous research, tuning, and monitoring, for returns that remain uncertain.
Meanwhile, we're building something with clearer, more durable leverage: AI operations consulting for mid-market companies that are drowning in manual workflows. Clients bring a defined problem, and the work can be scoped and checked.
We decided our time was better spent on governed AI operations, where the work is scoped, the value can be checked, and the infrastructure we built carries over.
The focus math:
- • Trading: full-time attention required + uncertain returns
- • Advisory: explicit demand + measurable value + compounding relationships
- • Healthcare AI (OralMind): 10-year moat + zero automation in market
- • Agent orchestration: the infrastructure we already built, productized
What's Next
Three bets, all in motion:
Hermes Intelligence Platform. The agent orchestration system we built for ourselves is being productized. It runs automated research and content workflows and narrates them through a public audit stream. That infrastructure is the product — we're now wiring it to serve advisory clients directly.
Advisory. We're taking on a small number of clients in Q2 — digitally-native businesses that want an AI operations audit and implementation roadmap. No retainer bloat. Specific, scoped engagements with measurable outcomes. If you're interested, the intake form is live.
OralMind Dental AI. The dental space has almost no AI penetration at the practice level. Imaging analysis, patient workflow automation, billing — all still manual. We're building a focused SaaS product here because the problem is clear, the buyers are identifiable, and the regulatory path (FDA Class II) is navigable. First design partners being onboarded now.
The trading bot taught us a lot. It made us better infrastructure engineers. It made us think rigorously about risk, execution, and decision architecture. All of that transfers directly to what we're building now.
We're not walking away from systematic thinking. We're applying it somewhere it compounds harder.
Work with Tacavar
If your operation is running on manual workflows and you want an honest assessment of where AI actually helps — fill in the intake form. We respond to every submission personally.