FinRL®: Financial Reinforcement Learning. 🔥
- deep-reinforcement-learning
- drl-trading-agents
- stock-trading
- drl-algorithms
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Scalable, event-driven, deep-learning-friendly backtesting library
- reinforcement-learning
- deep-reinforcement-learning
- gym-environment
- openai-gym
Environment for reinforcement-learning algorithmic trading models
Classify music genre from a 10 second sound stream using a Neural Network.
- deep-learning
- music-genre-classification
- paper
- tensorflow
TranslatedAI Paper System · AI paper generation, AI paper writing, one-click paper generation, AIGC rate reduction, plagiarism similarity reduction, thesis defense PPT · OEM white-label franchise recruitment
- ai
- ai-humanizer
- ai-ppt
- aigc
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
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My algorithmic trading strategies with the Gekko cryptocurrency trading bot.
- gekko-strategies
- trading-strategies
- machine-learning-algorithms
- neural-networks
Open BTC 5-minute prediction market sandbox for AI agents — register with one API call, get 1,000 test credits, and place a live order
- ai-agents
- prediction-markets
- starter-template
- trading-bot
A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.
- art-history
- color-analysis
- color-extraction
- color-palette