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Activating Auto-Reinvestment on UNI Grid

2026-01-18


What changed

I enabled automatic profit reinvestment on my UNI grid bot.

Instead of withdrawing grid profits as they’re generated, the system now reinvests accumulated arbitrage gains back into the grid on a daily schedule, slightly increasing position size over time.

This marks a shift from profit harvesting to compounding mode.


Why I made this change

This decision wasn’t about reacting to short-term performance. It was about aligning the bot’s behavior with the market environment and the purpose of the experiment.

Grid strategies tend to perform best during:

  • sideways or range-bound price action
  • repeated volatility without sustained breakout
  • conditions where mean reversion dominates trend

In those environments, reinvesting profits allows the grid to:

  • gradually scale order size
  • increase absolute profit per completed grid
  • capture a compounding effect without adding new capital

What I’m testing

This change turns the bot into a compounding experiment, not just a cash-flow generator.

I’m specifically observing:

  • whether reinvestment meaningfully increases grid efficiency over time
  • how exposure grows relative to price position within the range
  • whether compounding outperforms periodic manual withdrawals in similar conditions

This is a process test, not an optimization race.


Risk awareness

Automatic reinvestment does increase exposure if price trends strongly in one direction or exits the grid range.

Because of that:

  • price range discipline matters more
  • this setting will be revisited if UNI approaches range extremes
  • reinvestment is not a permanent default — it’s conditional

Automation is useful, but only when paired with review.


Current posture

For now, this bot is intentionally set to:

  • remain fully automated
  • compound during chop
  • prioritize learning over extraction

Results will be reviewed as part of my regular weekly lab process.


This is an experiment, not financial advice. Capital is allocated deliberately, outcomes are tracked, and assumptions are always subject to revision.