I will ai quantitative trading bot

This project delivers a custom AI trading system built using TensorFlow, OpenAI Gymnasium, RL, and LSTM-based recurrent architectures, specifically designed for financial time-series learning under noisy and biased market conditions.

The core focus of this work is reducing policy bias, improving directional accuracy, and stabilizing learning in environments where most trading agents failparticularly in highly unidirectional or skewed market regimes, where standard RL agents often collapse into trivial or biased strategies.

Key outcomes from this approach include:

Designing near-unbiased PPO agents even when trained on heavily skewed market data

Reducing sensitivity to short-term price noise that typically causes agents to lock capital in the wrong direction

Achieving stable training without reward clipping or information loss

Handling market non-stationarity using recurrent (LSTM-based) policies

The result is a research-grade, market-agnostic trading agent that can be trained on historical data and then used to predict actions across different markets such as stocks, crypto, or forex.