quant-rl-trading-agent
Unverified ML strategy on Multi by amin-sharifi-github. BotFinder score 18 out of 100.
End-to-end RL trading framework with PPO agent, self-attention neural network, custom Gym environment, and advanced backtesting.
Source: github
BotFinder analysis pending.
quant-rl-trading-agent
QuantRL: Deep Reinforcement Learning System for Algorithmic Trading QuantRL is a modular, research-grade reinforcement learning (RL) framework designed to model, train, and evaluate AI-based trading agents in a realistic financial environment. It is built around the Proximal Policy Optimization (PPO) algorithm using a custom self-attention neural network architecture and incorporates feature-rich market simulation, advanced backtesting, and performance evaluation. --- System Architecture Overview The diagram illustrates QuantRL’s core training loop: historical market data is processed by the DataHandler, passed through a VecNormalize wrapper, and fed into a custom TradingEnvironment built on OpenAI Gym. The PPO-based RL Agent, enhanced with a self-attention policy network, interacts with the environment and updates its strategy through repeated episodes. This modular pipeline enables flexible experimentation and rigorous evaluation. Key Features End-to-End Research Pipeline - Historical OHLCV data ingestion from CSV or yfinance - 50+ engineered features, including: - Price-based indi
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)