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• Dr. Thomas Starke – Deep Reinforcement Learning in TradingApply reinforcement learning to create, backtest, paper trade and live trade a strategy using two deep learning neural networks and replay memory.
• Learn to quantitatively analyze the returns and risks.
• Hands-on course in Python with implementable techniques and a capstone project in financial markets.LIVE TRADINGList and explain the need for reinforcement learning to tackle the delayed gratification experimentDescribe states, actions, double Q-learning, policy, experience replay and rewards.Explain exploitation vs exploration tradeoffCreate and backtest a reinforcement learning modelAnalyse returns and risk using different performance measuresPractice the concepts on real market data through a capstone projectExplain the challenges faced in live trading and list the solutions for themDeploy the RL model for paper and live tradingSKILLS COVEREDFinance and Math SkillsSharpe ratioReturns & Maximum drawdownsStochastic gradient descentMean squared errorPythonPandas, NumpyMatplotlibDatetime, TA-libFor loopsTensorflow, Keras, SGDReinforcement LearningDouble Q-learningArtificial Neural NetworksState, Rewards, ActionsExperience ReplayExploration vs ExploitationPREREQUISITES This course requires a basic understanding of financial markets such as buying and selling of securities.
• To implement the strategies covered, the basic knowledge of “pandas dataframe”, “Keras” and “matplotlib” is required.
• The required skills are covered in the free course, ‘Python for Trading: Basic’, ‘Introduction to Machine Learning for Trading’ on Quantra.