Practical AI for Finance with Python
Build Intelligent solutions for Quantitative Finance with modern Python libraries
Artificial Intelligence techniques for classifying and evaluating risk have an important place in the world of quantitative finance because of their greater power than classical statistical methodologies. Building models is a fluid and creative activity. There are many ways to build a model. The ability to create and understand models is one of the most valued skills in business and finance today. This expertise is highly valued in the area of Quantitative Finance, where numbers are important. Whether one is a veteran, just starting out a career, or still in school, having this expertise can give a competitive advantage.
The course combines relevant material from quantitative finance and sets up its interface to Artificial Intelligence techniques. The intent of this course is to show the tools—the vocabulary and the syntax of model building, for developing a model that works properly. Also, provides with a strong foundation for developing other models.
By the end, you will have a complete understanding of the applications of Artificial Intelligence for Quantitative Trading, Portfolio Theory, and Investment.
What you'll learnand how you can apply it
 Apply Artificial Intelligence techniques to solve Quantitative Finance problems such as, Portfolio Theory, Investment Analysis, Derivative Pricing and Risk Management
 Get familiar with various financial instruments
 Understand market and trading strategies to develop solutions
 Create a working, dynamic financial model to make correct projections
 Understand the mathematical and theoretical background of each topic covered
 Build models for HighFrequency Trading based
 Build models for Risk Management techniques such as Value at Risk and Extreme Value Theory
This training course is for you because...
You are a Quant Finance practitioner, HFT Trader, Algorithmic Trader, Portfolio Managers, Financial Engineer or Student who would like to expand your existing skillsets and knowledge with Artificial Intelligence concepts, practical modeling techniques and best practices in the industry.
Prerequisites
 An interest in solving Quantitative Finance problems
 Familiarity with Python programming language
 Familiarity with Data Science & Artificial Intelligence terminologies
Materials, downloads, or Supplemental Content needed in advance:
 Installed python development environment
 Initial exploration of the Python language – without the requirement of complete understanding of python
Recommended Preparation
About your instructor

Atul Tripathi has spent more than 16 years in the fields of artificial intelligence, machine learning, and quantitative finance. He has researched, worked and developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks.
He is the author of a book titled Machine Learning Cookbook by PACKT Publication. The book has been translated into Chinese.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Day 1
Section 1: Investment Portfolio Optimization and Efficient Frontier using Python  10 mins
 Theory about the application of Python programming for implementing Efficient Frontier, Sharpe Ratio, Monte Carlo
Lab: Building the application using Python – 35 mins
 Explore historical data which can be leveraged to choose specific mixes of assets based on investment goals
 Application of the Monte Carlo Method for optimizing a portfolio
 Evaluate assets based on randomly varying weights
 Plot expected annual return versus the historical volatility of the portfolio
 Plot each point which represents portfolio according to the Sharpe Ratio
 Apply structured approach for selection of asset weights  consider efficient portfolios meeting criteria important to the investor
 Find the optimum portfolio when an investor is faced with choosing from many assets
Q&A  5 mins
Break – 10 mins
Section 2: Statistical Learning approach for predicting stock market trends  10 mins
 Theory about Neural Network and stock market predictions
Lab: Building the application using Python – 35 min
 Use statistical learning approach for prediction of stock market trends
 Make useful stock price predictions
 Supplement trading strategies with predictions
Q&A  5 mins
Break – 10 mins
Section 3: Machine Learningbased Time Series Prediction for market price for given the currency  15 mins
 Theory about Time Series and currency market
Lab: Building the application using Python – 35 min
 Use collection of artificial intelligence methods to learn from the training data
 Build models using Ordinary linear model
 Build models using Gradient boosting
 Build models using Deep neural network
 Build models using Recurrent neural network: LSTM, GRU, one or multilayered
 Build models using Convolutional neural network for 1dimensional data
Q&A  5mins
Break – 10 mins
Section 4: Monte Carlo Simulations for pricing American and European Options using the Binomial Option Pricing Model  15 mins
 Theory about Monte Carlo Simulations, American Options, European options, Binomial Trees
Lab: Building the application using Python – 35 min
 Trade real options European and American
 Simulations using Monte Carlo Simulations
 Simulating Binomial Trees
Q&A  5 mins
DAY 2
Section 5: Machine Learningbased pairs trading strategy  10 mins
 Theory about Stochastic Volatility, Gaussian Process Regression, Recurrent Neural Network, Moving Average Reversion and pairs trading strategy
Lab: Building the application using Python – 35 min
 Identifying similar pairs of stocks
 Use of Long ShortTerm Memory (LSTM) Recurrent Neural Network (RNN) for training on the closing price of time series data
 Application of the Bayesian model to describe the timevarying nature of volatility in which the returns are Tdistributed with a variance that follows a Gaussian Random Walk
 Application of OnLine Moving Average Reversion
Q&A  5 mins
Break – 10 mins
Section 6: Monte Carlo Simulations for pricing Greeks using Antithetic Variance Reduction Techniques  10 mins
 Theory about Monte Carlo Simulations, Greeks, Antithetic Variance Reduction Techniques
Lab: Building the application using Python – 35 min
 Greeks pricing strategies
 Simulations using Monte Carlo Simulations
 Variance Reduction Techniques
Q&A  5 mins
Break – 10 mins
Section 7: Signal detection for quantitative trading strategies  10 mins
 Theory about MACD oscillator, HeikinAshi rules, London Breakout, Dual thrust Parabolic SAR, Bollinger Bands Pattern Recognition, Relative Strength Index Pattern Recognition
Lab: Building the application using Python – 35 min
 Use the signaling techniques and pattern recognition to learn about trading strategies
 Use momentum trading, opening range breakout and statistical arbitrage strategies
Q&A  5 mins
Break – 10 mins
Section 8: Artificial Intelligence based highfrequency algorithmic trading module  15 mins
 Theory about Qlearning and highfrequency algorithmic trading
Lab: Building the application using Python – 40 min
 Use of a stack of financial indicators which is consumed by a Qlearning algorithm which determines an Agent's action at a given step in the stream of financial quotes
 Sampling of the time series quotes to discover trends along any sort of time interval
Q&A  5 mins