Dr. Thomas Starke – Deep Reinforcement Learning in Trading

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. To gain an in-depth understanding of Neural Networks, you can enroll in the ‘Neural Networks in Trading’ course which is recommended but optional.Deep Reinforcement Learning in Trading by Dr. Thomas Starke, what is it included (Content proof: Watch here!)Section 1: IntroductionSection 2: Need for Reinforcement LearningSection 3: State, Actions and RewardsSection 4: Q LearningSection 5: State ConstructionSection 6: Policies in Reinforcement LearningSection 7: Challenges in Reinforcement LearningSection 8: Initialise Game ClassSection 9: Positions and RewardsSection 10: Input FeaturesSection 11: Construct and Assemble StateSection 12: Game ClassSection 13: Experience ReplaySection 14: Artificial Neural Network ConceptsSection 15: Artificial Neural Network ImplementationSection 16: Backtesting LogicSection 17: Backtesting ImplementationSection 18: Performance Analysis: Synthetic DataSection 19: Performance Analysis: Real World Price DataSection 20: Automated Trading StrategySection 21: Paper and Live TradingSection 22: Capstone ProjectSection 23: Future EnhancementsSection 24 (Optional): Python InstallationSection 25: Course SummaryABOUT AUTHORDr. StarkeDr. Starke has a Ph.D. in Physics and currently leads the quant-trading team in one of the leading prop-trading firms in Australia, AAAQuants, as its CEO. He has also held the senior research fellow position at Oxford University. Dr. Starke has previously worked at the proprietary trading firm Vivienne Court, and at Memjet Australia, the world leader in highspeed printing. He has led strategic research projects for Rolls-Royce Plc (UK) and is also the co-founder of the microchip design company.WHY QUANTRA?Gain more in less timeGet taught by practitionersLearn at your own paceGet data & strategy models to practice on your ownUSER TESTIMONIALSManogane RammalaGraduate in Investment Management, University of PretoriaIn its current form, the course is already comprehensive to a very high degree. All of the content in sections 1, 2, and 3 really helps in building an understanding regarding the deep RL trading system. I would compare this course to a suit that would have to grow into. I am going to revisit the section on ‘experience replay’ to get a better grip on that subject matter. The capstone project will also be very educational from the perspective of experimentation. To summarize, I’d say that this course will be the greatest learning material for RL in the financial markets for a very long time. Thank you for making it available!Vignesh PatelSenior Associate, Cognizant, IndiaDeep Reinforcement Learning as a concept is vast and complex. In this course, the content is broken down into smaller specific topics that help you grasp the subject at hand. In the end, everything is bought back together seamlessly for you to see the full picture clearly. I love how the complex concepts are made easy to understand, so much so that I was able to do the capstone project at the end of the course all by myself. I only referred to the model solution after I successfully made the model in the capstone project on my own. This course has definitely increased my understanding and clarity on Deep Reinforcement learning.Vinod PandiripalliData Scientist, Franklin Templeton. IndiaThe Deep Reinforcement Learning course has definitely opened a gate and brought me closer to my goal to achieve financial independence. It has given me great confidence in the area of Algorithmic Trading. The course is organised and designed in such a way that it made it easier for me to grasp the topics faster. The course was divided into smaller modules, which further helped me understand the concepts in greater depth. This course is a complete package, everything that you need to learn, is already available in the course. As a Data Scientist, was also able to upskill myself in the same domain, all thanks to this course.Sale Page: https://quantra.quantinsti.com/course/deep-reinforcement-learning-tradingArchive: https://archive.ph/wip/mIAQHDelivery Method– After your purchase, you’ll see a View your orders link which goes to the Downloads page. Here, you can download all the files associated with your order.– Downloads are available once your payment is confirmed, we’ll also send you a download notification email separate from any transaction notification emails you receive from esy[GB].– Since it is a digital copy, our suggestion is to download and save it to your hard drive. In case the link is broken for any reason, please contact us and we will resend the new download link.– If you cannot find the download link, please don’t worry about that. We will update and notify you as soon as possible at 8:00 AM – 8:00 PM (UTC+8).Thank You For Shopping With Us!