bugs

A Practical History, What It Does, and Where It Might Go
This trading bot did not start as a product, a signal service, or an attempt to “beat the market.”
It started as a question:
Can a small, rule-based system survive real market conditions long enough to teach us something true?
Not outperform. Not predict. Survive, adapt, and remain observable.

The Early Phase: Assumptions Meet Reality
The first iterations were simple by design.
A constrained universe of liquid USDC pairs, basic momentum and structure filters, conservative position sizing, and strict notional limits. The goal was not cleverness — it was controllability.
Almost immediately, reality intervened.
Execution friction mattered more than expected.
Dust accumulated in places that backtests never warned about.
Small fees and rounding errors quietly eroded results.
Some trades “worked” directionally but still lost money.
These were not #bugs . They were #Lessons .
The early PnL oscillated around flat, with small drawdowns measured in low single-digit percentages. Nothing dramatic — and yet emotionally instructive. A −0.6% day can feel louder than a +1.2% day when it’s your system and your assumptions on trial.

What the Bot Actually Does (Today)
At its core, the bot is a rule-based execution system, not a predictive engine.
It operates by:
selecting a constrained trading universescoring candidates based on observable market structureentering positions with predefined risk boundariesmanaging exits through deterministic logic rather than discretion

No trade is taken because the system “believes” something.
Trades are taken because conditions match predefined criteria.
Risk is sized before entry.
Losses are accepted as part of operation, not as failures to be avoided at all costs.
Importantly, the bot trades live, under real exchange conditions:
real spreadsreal latencyreal feesreal minimum notionals

Every result is therefore a record of behavior, not theory.

Setbacks That Shaped the System
Several iterations produced worse results than their predecessors.
Some changes improved win rate but increased drawdowns.
Others reduced volatility but also reduced opportunity.
A few “clever” ideas turned out to be pure overfitting once exposed to live data.
One recurring lesson:
complexity is easy to add and hard to justify.
Each setback forced the same discipline:
isolate what changedobserve the outcomedecide whether the trade-off was worth it
In many cases, the best decision was to revert.

Why PnL Is Shared Publicly
PnL is not shared as proof of skill.
It is shared as an audit trail.
Without PnL, system development drifts into narrative.
With PnL, every claim is anchored to consequences.
Flat periods, small losses, and underwhelming improvements are part of the record because they reflect reality more accurately than selective success.
There are no hidden “off-book” experiments and no reset of history.

What This Bot Is Not
It is not a signal provider.
It is not optimized for short-term returns.
It is not immune to drawdowns.
It does not claim an edge that cannot disappear.
It is also not finished — and may never be.
Outlook: Evolution Without Illusions
Future development will likely focus on:
better characterization of risk regimesreducing fragility under changing volatilityimproving exit behavior rather than entry clevernessresisting unnecessary complexity
Some changes will help.
Some will fail quietly.
Others may introduce new problems we don’t yet understand.
That uncertainty is not a weakness — it is the most honest state a system can be in.
Closing Thoughts

This bot is an ongoing experiment in restraint.
In a space crowded with certainty, it chooses documentation.
In a market that rewards stories, it records behavior.
In a culture of promises, it settles for evidence.
If it continues to evolve, it will be because the data allowed it — not because we wanted it to.
And if it stops working, that outcome will be published too.
Because the only real edge here is not prediction —
it’s refusing to lie to ourselves.
Follow me for the journey and the outcome