AI Market Intelligence & Technical Analysis Engine
Our trading startup needed an internal tool that could process large volumes of historical price data, calculate technical indicators, and produce strategy-aligned reports - without manual interpretation slowing us down.
Large-scale data processing
Ingested and processed years of historical price action across multiple markets and timeframes.
Strategy-aligned AI reports
AI models generated reports cross-referenced against curated trading strategies with tunable parameters.
End-to-end AI workflow
Built complete pipelines from raw data ingestion through analysis to actionable, fine-tuned outputs.
Approach
Built an analysis engine that ingested price action, computed technical analysis values, and ran AI models to generate reports cross-referenced against our curated trading strategies - with fine-tuning capabilities baked in.
Outcome
Created a repeatable system for market intelligence that informed strategy development and taught me valuable lessons about handling large datasets, orchestrating AI workflows, and tuning AI outputs to match specific trading frameworks.
The need
Inside the trading startup, we had strong execution capabilities but no reliable way to systematically analyze market conditions. Manual chart review was time-consuming and inconsistent - different people read the same data differently.
We needed an internal tool that could ingest historical price action, run technical analysis calculations, and produce structured reports aligned with the specific trading strategies we were developing.
The goal was to create a repeatable intelligence layer that could inform strategy decisions without relying on subjective interpretation.
What I built
I built an internal analysis engine that imported historical price data and calculated a range of technical analysis values - moving averages, volatility metrics, momentum indicators, trend structures, and pattern recognition.
From there, the system ran various scripts and AI models through the processed data to generate human-readable reports. These reports were cross-referenced against specific trading strategies we were focusing on, with the ability to fine-tune the outputs based on our curated frameworks.
The architecture allowed us to adjust how the AI weighted different indicators, which strategies to prioritize, and how aggressive or conservative the interpretations should be.
This meant the team could get consistent, strategy-aligned market reads without spending hours manually synthesizing charts and indicators.
Challenges & lessons
The biggest challenges I faced were around handling large data volumes efficiently, orchestrating complex AI workflows, and tuning AI responses to produce outputs that actually aligned with our curated strategies.
Processing years of historical data across multiple timeframes required careful attention to data pipelines, storage efficiency, and computation speed. Getting the AI to produce useful, strategy-specific insights - rather than generic summaries - took iteration and refinement.
These lessons have shaped how I approach AI-powered tooling today: understanding that the real work isn't just connecting an AI model, but building the data infrastructure around it and tuning outputs to match specific business contexts.