AI Personal Trading Platform
An experimental AI-assisted market research & paper-trading lab.
Regime signal
Neutral
The idea
Markets are noisy and full of opinions. I wanted a private space to study them on my own terms — to test ideas with real discipline, keep the research honest, and separate genuine edge from wishful thinking. The result is a personal research lab, not a product sold to anyone.
The problem
Most tooling either hides the reasoning behind a black box or drowns you in raw data. I wanted something in between: clear signals, explainable logic, and guardrails that keep experiments safe while I learn what actually works.
What I built
A local-first dashboard that pulls read-only market data, runs strategy research and backtesting, and simulates paper trades with enforced risk controls. It is deliberately paper-only — no live brokerage orders are placed by the system.
A safe research loop
Data flows in read-only, research runs locally, and every action stays inside a paper environment. Credentials and keys live only on the local machine — nothing sensitive is published or exposed.
AI-assisted market analysis
Multi-agent workflows summarize context and surface study candidates — always as research, never as instructions.
Strategy research & backtesting
Regime-gated strategies tested against historical data with conservative, explainable scoring.
Paper trading engine
Simulated fills and monitored positions starting from a fixed test balance, with strict stop/target logic.
Deterministic risk controls
Position limits, exposure caps, and a kill-switch hierarchy. Hard safety rules never depend on the AI layer.
Read-only broker integration
Market and account data arrive through read-only APIs. The system cannot submit live orders.
Monitoring & alerts
Scheduled checks and Telegram notifications keep the research loop running without constant manual attention.
AI assists. Rules decide.
Independent AI workers help with market-context summarization, candidate research, and coaching-style notes on closed paper trades. They are observational only.
The final decisions — entries, exits, sizing, and risk — are made by deterministic code and explicit controls. The AI layer can suggest and explain, but it cannot relax a stop, submit an order, or change the safety posture. This separation is the whole point of the design.
Automation handles the repetitive loop: scheduled research refreshes, monitoring, and notifications — so the lab keeps working even when I'm not watching it.
Strategy & research
Several strategy styles are explored in parallel — from broad-universe, regime-gated approaches to more focused momentum and volatility research. Each is backtested, paper-tested, and tracked with conservative readiness gates before it earns any trust.
Performance is treated as a hypothesis to be challenged, not a number to be advertised. Small samples are labeled inconclusive, and no claims of guaranteed results are ever made.
Always improving
The platform is a living experiment. New strategies, better research tooling, and tighter controls are added over time, with each change validated before it can affect anything.
It remains a personal research environment — a place to learn how markets behave and how automation can help, without risking real capital.
Built with
The system is implemented in Python for research, data, and automation, with a web dashboard for exploration. Market data is integrated read-only, and automation runs through scheduled jobs with monitoring and alerting.