Python for Finance and Algorithmic trading, 2nd edition: Machine Learning, Deep Learning, Time series Analysis, Risk and Portfolio Management for MetaTrader™5 Live Trading

Table of contents
Why should you read this book?
    Chapter 1: Read me
        1.1. Find the code
        1.3. Join our community
Chapter 2: Prerequisites
    2.1. Mathematical prerequisites
        2.1.1. Algebra
        2.1.2. Statistics
        2.1.3. Optimization
    2.2. Prerequisites in finance
        2.2.1. Market efficiency
        2.2.2. Basics of trading
        2.2.3. Basics of portfolio management
    2.3. Prerequisites in Python
        2.3.1. Libraries for data sciences
        2.3.2. Libraries for finance
        2.3.3. Libraries for mathematics
    Part 1: Portfolio management, risk management and backtesting
Chapter 3: Static Portfolio management
    3.1. Explanation behind portfolio optimization method
        3.1.1. Systemic risk and specific risk
        3.1.2. Diversification
    3.2. The traditional portfolio optimization methods
        3.2.1. Portfolio utility function
        3.2.2. Mean-variance criterion
        3.2.3. Mean-variance-skewness-kurtosis criterion
    3.3. The modern portfolio optimization methods
        3.3.1. Sharpe criterion
        3.3.2. Sortino criterion
Chapter 4: Tactical portfolio management
    4.1. How dynamic methods works?
        4.1.1. Short a stock
        4.1.2. Momentum factor
        4.1.3. Rebalancing
    4.2. Moving average strategy
        4.2.1. Moving average
        4.2.2. Moving average factor
        4.2.3. Build the strategy
    4.3. Trend following strategy
        4.3.1. Correlation
        4.3.2. Trend following factor
        4.3.3. Compute the strategy
Chapter 5: Risk management and backtesting
    5.1. The backtesting metrics
        5.1.1. The CAPM metrics
        5.1.2. Sharpe and Sortino
        5.1.3. Drawdown
    5.2. Risk management metrics
        5.2.1. Value at risk (VaR)
        5.2.2. Conditional Value at risk (cVaR)
        5.2.3. Contribution risk
    5.3. Automate the analysis
        5.3.1. Create a function
        5.3.2. Analyze static portfolio
        5.3.3. Analyze dynamic portfolio
Chapter 6: Advanced backtest methods
    6.1 Useful backtest advice
        6.1.1 Backtest is not a searching tool
        6.1.2 Big days are not your friends
        6.1.3 Understand your strategy
    6.2 Compute the strategy returns using TP and SL
        6.2.1 Find the extremum
        6.2.2 Calculate the returns
        6.2.3 Analyze the backtest
    6.3 Advanced backtest tools
        6.3.1 Backtest metrics list
        6.3.2 Monte Carlo simulation
        6.3.3 Easy Trailing stop
    Part 2: Statistics predictive models
Chapter 7: Statistical arbitrage Trading
    7.1. Stationarity to Cointegration
        7.1.1. Stationarity
        7.1.2. Cointegration
    7.2. Pairs trading
        7.2.1. How it works
        7.2.2. Applications
Chapter 8: Auto Regressive Moving Average model (ARMA)
    8.1. Time series basics
        8.1.1. Trend, cycle, seasonality
        8.1.2. Log price properties
        8.1.3. The linear regression
    8.2. AR and MA models
        8.2.1. Autoregressive model (AR)
        8.2.2. Moving average model (MA)
    8.3. ARMAs models
        8.3.1. ARMA model
        8.3.2. ARIMA model
Chapter 9: Linear regression and logistic regression
    9.1. Regression and classification
        9.1.1. Reminder about regression
        9.1.2. Understand the classification
    9.2. Linear regression
        9.2.1. Preparation of data
        9.2.2. Implementation of the model
        9.2.3. Predictions and backtest
    9.3. Logistic regression
        9.3.1. Preparation of data
        9.3.2. Implementation of the model
        9.3.3. Predictions and backtest
    Part 3: Machine learning, deep learning, live trading
Chapter 10: Features and target engineering
    10.1 Motivation and intuition
        10.1.1 Features engineering
        10.1.2 Target engineering
        10.1.3 Why is it so important?
        10.2 Trading application
        10.2.1 Create trading indicators and useful trading features
        10.2.2 Target labeling
Chapter 11: Support vector machine (SVM)
    11.1. Preparation of data
        11.1.1. Features engineering
        11.1.2. Standardization
    11.2. Support Vector Machine Classifier (SVC)
        11.2.1. Intuition about how works an SVC
        11.2.2. How to create an SVC using Python
        11.2.3. Predictions and backtest
    11.3. Support Vector Machine Regressor (SVR)
        11.3.1. Intuition about how works an SVR
        11.3.2. How to create an SVR using Python
        11.3.3. Predictions and backtest
Chapter 12: Ensemble methods and decision tree
    12.1. Decision tree
        12.1.1. Decision Tree classifier
        12.1.2. Decision tree regressor
        12.1.3. Optimize the hyperparameters
    12.2. Random Forest
        12.2.1. Random Forest classifier
        12.2.2. Random Forest Regressor
        12.2.3. Optimize the hyperparameters
    12.3. Ensemble methods
        12.3.1. Voting method
        12.3.2. Bagging method
        12.3.3. Stacking method
Chapter 13: Deep Neural Networks (DNN)
    13.1. Intuition behind DNN
        13.1.1. Forward propagation
        13.1.2. Gradient descent
        13.1.3. Backpropagation
    13.2. DNN for classification
        13.3.1. Preparation of data
        13.2.2. Implementing a DNN for a classification task
        13.2.3. Prediction and Backtest
    13.3. DNN for regression
        13.3.1. Implementing a DNN for a regression task
        13.3.2. Custom loss function
        13.3.3. Prediction and Backtest
Chapter 14: Recurrent neural network
    14.1 Principles of RNN
        14.1.1. How an RNN works
        14.1.2. LSTM neuron
        14.1.3. GRU cell
    14.2. RNN for classification
        14.2.1. Transform 2d data to 3d data
        14.2.2. Implementing the model
        14.2.3. Prediction and backtest
    14.3. RNN regressor
        14.3.1. Precision about standardization
        14.3.2. Implementing the model
        14.3.3. Predictions and backtest
Chapter 15: Bonus / Example of RNN with CNN (RCNN)
    15.1. Intuition of CNN
    15.2. Create an RCNN
    15.3. Backtest
Chapter 16: Real-life full project
    16.1. Preparation of the data
        16.1.1. Import the data
        16.1.2. Features engineering
        16.1.3. Train, test and validation sets
    16.2. Modelling the strategy
        16.2.1. Find the best assets
        16.2.2. Combine the algorithms
        16.2.3. Apply portfolio management technics
    16.3. Find optimal take profit, stop loss and leverage
        16.3.1. Optimal take profit (tp)
        16.3.2. Optimal stop loss (sl)
        16.3.3. Optimal leverage
Chapter 17: From nothing to live trading
    17.1 Trading strategies creation guidelines
        17.1.1 The trading plan
        17.1.2 The process of a trading strategy building
        17.1.3 Trading journal
    17.2 Do not put all your eggs in one basket
        17.2.1 Bet sizing
        17.2.2 Why create a trading strategy portfolio?
    17.3. Live trading process
        17.3.1. Safety first
        17.3.2 Incubation phase
        17.3.3 When to quit?
Annex: Compounding versus simple interest
    What is the difference between the simple and the compounding methods?
    How to compute the simple and compounding interest
Annex: Save and load scikit-learn and Tensorflow models
Annex: MetaTrader class
Additional readings