FlyTV: A Connectome-Inspired Trading Strategy Using Neural Circuits
Summary
FlyTV maps selected circuits from a measured adult fruit fly connectome into a simulated neural network driven by market candles. Its modeled sensory input emphasizes price declines, rises, and bar range. Looming detectors can trigger an escape response that closes positions and starts a cooldown; other pathways support long entries, while a ring attractor represents market direction and is compared with a momentum-based goal. Kenyon cell and mushroom body output neuron activity provides a simple adaptive memory signal.
The document distinguishes measured wiring and synaptic properties from modeled firing, candle inputs, and order generation. It describes a strategy implementation and design choices, not evidence that real fly physiology predicts markets. The author reports that the system does not outperform buy and hold. Its reported neuron and connection counts vary between the introductory description and embedded strategy data, and no independent performance evaluation is provided.
Key ideas
- The strategy translates measured fruit fly neural connections into a simplified network driven by candle data.
- Looming signals can flatten the position and impose a cooldown, while other modeled circuits generate long-side behavior.
- A ring attractor encodes direction, which is compared with a momentum-based goal to guide steering.
- Adaptive memory is represented through Kenyon cell and mushroom body output activity.
- Measured connectome structure does not validate the modeled neural dynamics or establish trading effectiveness.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.