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This is an extensive course for the gann trader as well as the investor. W. D. Gann's Stock Trading Course can teach you a number of different trading techniques and skills, such as charting, chart interpretation, how do find natural resistance levels, forecasting trend changes, using Gann Lines (or Gann Angles), seasonal changes for stocks, how to decipher time cycles, the relationship between time and price, squaring price and time, how to use gann squares & gann calculators and more.
Richard W. Schabacker's great work, Technical Analysis and Stock Market Profits, is a worthy addition to any technical analyst's personal library or any market library. His "pioneering research" represents one of the finest works ever produced on technical analysis, and this book remains an example of the highest order of analytical quality and incisive trading wisdom. Originally devised as a practical course for investors, it is as alive, vital and instructional today as the day it was written. It paved the way for Robert Edwards and John Magee's best-selling Technical Analysis of Stock Trends - a debt which is acknowledged in their foreword: 'Part One is based in large part on the pioneer researches and writings of the late Richard Schabacker.'Schabacker presents technical analysis as a totally organized subject and comprehensively lays out the various important patterns, formations, trends, support and resistance areas, and associated supporting technical detail. He presents factors that can be confidently relied on, and gives equal attention to the blemishes and weaknesses that can upset the best of analytical forecasts: Factors which investors would do well to absorb and apply when undertaking the fascinating game of price, time and volume analysis.
Included in this volume are Louise McWhirter's theories and numerous, fully-explained and detailed examples for: Forecasting business cycles and stock market trends, forecasting trends of individual stocks, and forecasting monthly and daily trends on the New York stock exchange.
Stock Market Modeling and Forecasting translates experience in system adaptation gained in an engineering context to the modeling of financial markets with a view to improving the capture and understanding of market dynamics. The modeling process is considered as identifying a dynamic system in which a real stock market is treated as an unknown plant and the identification model proposed is tuned by feedback of the matching error. Like a physical system, a financial market exhibits fast and slow dynamics corresponding to external (such as company value and profitability) and internal forces (such as investor sentiment and commodity prices) respectively. The framework presented here, consisting of an internal model and an adaptive filter, is successful at considering both fast and slow market dynamics. A double selection method is efficacious in identifying input factors influential in market movements, revealing them to be both frequency- and market-dependent. The authors present work on both developed and developing markets in the shape of the US, Hong Kong, Chinese and Singaporean stock markets. Results from all these sources demonstrate the efficiency of the model framework in identifying significant influences and the quality of its predictive ability; promising results are also obtained by applying the model framework to the forecasting of major market-turning periods. Having shown that system-theoretic ideas can form the core of a novel and effective basis for stock market analysis, the book is completed by an indication of possible and likely future expansions of the research in this area.
Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.
Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey - Leading thinkers present newest research on volatility forecasting - International authors cover a broad array of subjects related to volatility forecasting - Assumes basic knowledge of volatility, financial mathematics, and modelling
W. D. Gann's Commodities Trading Course is an extensive course. This course gives you a number of different trading techniques and skills. Which include: charting, chart interpretation, using Gann Angles, Squaring Price and Time, using Gann Squares, Square of Nine, Gann Numbers, Gann Calculators and more. This course consists of Gann's original course he sold in the early 1950's for a reported $5,000. Here is a listing of the subjects covered in this Course: Speculation; a Profitable Profession. Mechanical Method and Trend Indicator Rules for Trading in Grains The Basis of My Forecasting Methods for Grains Forecasting by Time Cycles. The Basis of My Forecasting Method for Cotton Mechanical Method and New Trend Indicator for Cotton Cash and May Soybean Futures Master Egg Course Master Charts Supplement Section
Practical, hands-on solutions in Python to overcome any problem in Machine Learning Key Features Master the advanced concepts, methodologies, and use cases of machine learning Build ML applications for analytics, NLP and computer vision domains Solve the most common problems in building machine learning models Book Description Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity. What you will learn Select the right algorithm to derive the best solution in ML domains Perform predictive analysis effciently using ML algorithms Predict stock prices using the stock index value Perform customer analytics for an e-commerce platform Build recommendation engines for various domains Build NLP applications for the health domain Build language generation applications using different NLP techniques Build computer vision applications such as facial emotion recognition Who this book is for This book is for the intermediate users such as machine learning engineers, data engineers, data scientists, and more, who want to solve simple to complex machine learning problems in their day-to-day work and build powerful and efficient machine learning models. A basic understanding of the machine learning concepts and some experience with Python programming is all you need to get started with this book.
In 1937 Louise McWhirter published her ground-breaking forecasting methodology in which she revealed how to forecast in advance the general state of the economy for years to come. She revealed how to use planetary angles present at the time of a New Moon to identify key dates in a lunar cycle when the New York Stock Exchange would have a high probability of exhibiting a price trend change. She further showed how to use planetary transits, angles and aspects to predict times when individual stocks and commodity futures would have a high probability of exhibiting a price trend change. Today, McWhirter's work in in danger of fading into a distant memory. This book has been crafted in part to help ensure that does not happen. This book has also been crafted to assist the trader and investor in de-mystifying the many nuances in the McWhirter methodology.