Description
Chapter 1: Introduction to the Financial Markets and Algorithmic Trading Foreign exchange market – Exchange rate – Exchange rates quotation The Interbank market The retail market Brokerage – Understanding leverage and margin – Contract for difference trading The share market Raising capital – Public listing – Stock exchange – Share trading Speculative nature of foreign exchange market Techniques for speculating market movement Algorithmic trading – Supervised machine learning The parametric method – The non-parametric method Binary classification Multiclass classification – The ensemble method – Unsupervised learning – Deep learning – Dimension reduction Chapter 2: Forecasting Using ARIMA, SARIMA and Additive Model Time series in action Split data into training and test data Test for stationary Test for white noise Autocorrelation function Partial autocorrelation function The moving averages smoothing technique The exponential smoothing technique Rate of return The ARIMA Model ARIMA Hyperparameter Optimization – Develop the ARIMA model – Forecast prices using the ARIMA model The SARIMA model – Develop SARIMA model – Forecast using the SARIMA model Additive model – Develop the additive model – Forecast prices the additive model – Seasonal decomposition Conclusion Chapter 3: Univariate Time Series using Recurrent Neural Nets What is deep learning? Activation function Loss function Optimize an artificial neural network The sequential data problem The recurrent net model The recurrent net problem The LSTM model Gates Unfolded LSTM network Stacked LSTM network LSTM in action – Split data into training, test and validation – Normalize data – Develop LSTM model – Forecasting using the LSTM – Model evaluation – Training and validation loss across epochs – Training and validation accuracy across epochs Conclusion Chapter 4: Discover Market Regimes HMM HMM application in finance – Develop GaussianHMM Mean and variance Expected returns and volumes Conclusions Chapter 5: Stock Clustering Investment Portfolio Diversification Stock market volatility K-Means clustering K-Means in practice Conclusions Chapter 6: Future Price Prediction using Linear Regression Linear Regression in Practice Detect missing values Pearson correlation Covariance Pairwise scatter plot Eigen matrix Split data into training and test data. Normalize data Least squares model hyperparameter optimization Step 1: Fit least squares model with default hyperparameters Step 2: Determine the mean and standard deviation of the cross-validation scores Step 3: Determine Hyper-parameters that yield the best score. Develop least squares model Find an intercept Find the estimated coefficient Test least squares model performance using SciKit-Learn Plotting actual values and predicted values Conclusion Chapter 7: Stock Market Simulation Understanding value at risk Estimate VAR using the Variance-Covariance Method Understanding Monte Carlo Application of Monte Carlo simulation in finance – Run Monte Carlo simulation – Plot simulations Conclusions Chapter 8: Market Trend Classification using ML and DL Classification in practice Data preprocessing Split Data into training and test data Logistic regression – Finalize a logistic classifier – Evaluate a logistic classifier – Learning curve Multilayer layer perceptron – Architecture – Finalize model – Training and validation loss across epochs – Training and validation accuracy across epochs Conclusions Chapter 9: Investment Portfolio and Risk Analysis Investment Investment Analysis Investment Risk Management Investment Portfolio Management Pyfolio in action Performance statistics Drawback Rate of returns Annual rate of return Rolling returns – Monthly rate of returns Conclusions
