Separate strategy approaches
Two decision paths make it possible to compare explicit custom logic with an AI-assisted exploration without treating either as an investment recommendation.
Engineering project
MEXC Crypto Trading Automation
A Python trading-automation project exploring exchange market-data collection, technical-indicator processing, defined order logic, and API error handling.
- 1Exchange market data
- 2Indicator and pattern processing
- 3Defined decision logic
- 4Order request
- 5API error and retry handling
Implementation scope
What this project implements
The project uses the MEXC API to collect market data and calculate technical indicators and candlestick-pattern inputs. It explored two distinct approaches to rule handling: custom logic and an OpenAI-assisted variant.
The application scope includes exchange authentication, data parsing, API-rate-limit handling, error handling, and retry behaviour around external requests.
It demonstrates the practical engineering questions behind connecting a client-defined strategy workflow to an exchange API, including order requests, latency, and connection boundaries.
Engineering choices
Clear boundaries in the application design
External API resilience
Error handling and retries acknowledge rate limits, connection issues, and the fact that third-party exchange responses are part of the operating boundary.
Data-to-order workflow
Market-data collection, processing, defined logic, and order requests are treated as connected but distinct stages.
Current boundaries
Important limitations
- Trading conditions, order handling, and API availability need deployment-specific validation.
- The work demonstrates software engineering only. It does not promise profitability or recommend an asset or strategy.