Lessons from Building a Leveraged Cryptocurrency Trading Bot

Summary

This retrospective follows a retail trader’s development of an automated cryptocurrency strategy, from learning JavaScript and adapting shared examples to running short-term trades in Litecoin and Bitcoin. The author reports an initially strong stretch, then losses after adding conditions, and later better results after removing a short-term trend filter. The account also describes shifting from less active Litecoin to more actively traded Bitcoin and using spot-market borrowing to increase exposure.

A small test of five-minute Bitcoin candles found that after a run of declines, the next candle fell slightly more often than half the time over the brief sample; the author notes that a longer history could produce a result nearer chance. Personal account curves and reported outcomes are not controlled evidence. The author links reduced exposure to lower risk and increased exposure to greater drawdowns, while the strategy’s settings, costs, and repeatability remain unclear.

Key ideas

• The author built a short-term automated strategy while learning JavaScript and using shared examples.
• Adding extra strategy conditions coincided with a losing period, while removing a trend filter preceded a recovery.
• The author favored Bitcoin over Litecoin because Bitcoin trading was more active.
• A brief candle-frequency test offered weak evidence about predictability and may not generalize.
• Borrowed exposure increased both potential gains and drawdown risk.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.