TEST BEFORE YOU TRUST
Research prototypeAI research and analysis
AI Trading Research
Turn market ideas into repeatable experiments.
A research environment connecting reinforcement-learning agents, backtesting, simulated trading, and risk analysis.
HOW IT COMES TOGETHER
The core workflow- 01
Market data
- 02
Policy training
- 03
Backtests and simulation
- 04
Results and risk
01 / THE QUESTION
The problem we set out to solve.
A strategy that looks promising on historical data still needs to be tested on later periods. Training, validation, trading costs, and risk should be considered in the same experiment.
02 / OUR APPROACH
Our approach.
We connect learning environments and agents with time-series validation and a simulated broker. A dashboard helps researchers inspect results, behavior, and limitations.
- 01
Reinforcement-learning experiments
PPO, A2C, and SAC agents interact with training environments to explore trading policies.
- 02
Time-series validation
Walk-forward validation and backtesting examine performance beyond the training period.
- 03
Simulation and risk
A simulated broker, portfolio components, and risk modules help review how a strategy behaves.
- 04
Research dashboard
A Streamlit interface presents experiment data and results for inspection.
03 / INTELLIGENCE AT WORK
Where AI does the work.
This project uses reinforcement learning rather than LLM content generation. Agents learn a policy through interaction with an environment, and their results are evaluated in a separate validation process.
04 / WHERE WE ARE
Where the project stands.
A research-stage project connecting learning agents, backtests, and simulated trading. Results are assessed through time-series validation and risk analysis; live-trading returns have not been established.
This project is for research and simulated evaluation. Historical results do not guarantee future returns.
