24option introduces the graphs for technical analysis

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26 Ways To Beat The Market Using Technical Analysis

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Introduction

Technical analysis forms the foundation of market speculation for trading in financial markets. It is also the product of hundreds of years of market data—concepts like candlestick techniques were developed as far back as the 18th century. In contrast to fundamental analysis, which examines aspects of a company’s stock such as earnings, dividends, ratios, and assets, those that employ technical analysis seek to find price patterns and market trends in order to profit off them.

This is no simple task.

The methods and techniques used can be overwhelming. A beginner should be prepared to enter the complex world of multiple indices, moving averages, trend following, indicators, and oscillators.

But where should you start and what does it all mean?

Behind all the charts, graphs, and mathematic formulas, there are basic concepts that apply to most of these tools and techniques. With a solid grasp on the fundamentals, any beginner can become a technical analyst and take his trading skills to new heights.

Listed below, this blog post has filtered out the noise and outlined the best resources out there in taking on the quest of mastering technical analysis.

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Technical Analysis Courses

  • Investing Teacher:https://investingteacher.com/

A first of its kind interactive experience that teaches stock charts start to finish. Investing Teacher provides completely interactive content including drawing trendlines on charts, drag and drop, built for tablets, interactive questions, and more. The guide consists of 28 chapters, teaching everything there is to know about stock charts. From the basics of what a stock chart is to identifying Cup & Handle formations.

Free site for beginners to technical analysis, covering a wide range of topics, indicators, and strategies.

Free for registered users this course covers trendlines, candlestick patterns, charts, reversal patterns, and more. Introduction to support and resistance, trend lines, chart patterns, harmonic patterns; how to use technical analysis to set stop-loss, limit orders, and take profit levels.

This free course offers a comprehensive introduction and overview of technical analysis, applying fundamental concepts and techniques to understand the foundation of the study. It provides a good foundation through 12 lessons including the use of trend, support and resistance, chart types, chart patterns, moving averages, indicators, and oscillators for the beginner in order to understand more advanced concepts down the road.

Technical Analysis Books

  • Technical Analysis Explained by Martin Pring

Considered by many to be the “Bible” of technical analysis. The book, in fact, goes well beyond just explaining technical analysis, addressing subjects such as the structure and interconnection of financial markets and trading psychology.

First published in the 1980s, the book remains a favorite with technical traders.

  • Encyclopedia of Chart Patterns by Thomas Bulkowski

The title of Thomas Bulkowski’s book is not an overstatement, as it does provide truly encyclopedic coverage of technical chart patterns by the well-known chartist and market analyst.

Bulkowski presents extensive explanations on virtually every candlestick charting pattern ever identified, and goes further by providing statistical information on how often each pattern has proved correct in predicting future price movements in both bull and bear markets.

The updated version of the book also includes a section on event trading that shows technical patterns related to news events such as major economic data releases and company earnings reports.

  • How to Make Money in Stocks by William O’Neill (Founder of Investor’s Business Daily)

Considered a classic work on technical analysis, and is a trading bestseller partly because it was written by William O’Neill, founder of the financial newspaper, Investor’s Business Daily. O’Neill, a strong advocate of technical trading methods, studied over 100 years of stock price movements, going all the way back to 1880, in researching the book.

In it, he presents a variety of technical strategies that can be applied to trading stocks, commodities, ETFs or the forex market, along with tips on minimizing risk and identifying the most advantageous risk/reward situations, and on identifying favorable entry and exit points for trades.

O’Neill also explains his own CAN-SLIM technical trading strategy that he used to amass a fortune through trading in the 1960s.

  • Technical Analysis Using Multiple Timeframes by Brian Shannon

Brian Shannon’s book has wide appeal for technical traders because it can be helpful to traders regardless of what specific technical trading strategy, or strategies, that they may be using. Shannon’s book points out the value of applying technical analysis across multiple timeframes in order to identify trades with the highest probability of success. The book also goes well beyond what its title indicates, including coverage of subjects such as short selling, proper placement of stop-loss orders and identifying target prices for maximally profitable trade exits.

  • Technical Analysis of Financial Markets by John J. Murphy

Considered as an outstanding reference that has already taught thousands of traders the concepts of technical analysis and their application in the futures and stock markets.

Covering the latest developments in computer technology, technical tools, and indicators, the second edition features new material on candlestick charting, intermarket relationships, stocks and stock rotation, plus state-of-the-art examples and figures. From how to read charts to understanding indicators and the crucial role technical analysis plays in investing, readers gain a thorough and accessible overview of the field of technical analysis, with a special emphasis on futures markets.

Revised and expanded for the demands of today’s financial world, this book is essential reading for anyone interested in tracking and analyzing market behavior.

Trend Following

How to Spot the Stock Market’s Trend Before It Is Obvious To All

This article highlights how to spot when times are changing “for real”, and a longer trend is developing, by focusing on the signs of underlying strength or weakness.

5 Ways to Identify the Direction of the Trend

This article introduces the most effective ways to analyze a chart to be able to correctly read price action, trends, and trend direction. It introduces different market phases, line graphs, moving averages, highs and lows, channels and trend lines, and the ADX indicator.

It argues that success in trend following lies in choosing your tools, how well you understand them, and how good you are when it comes to applying them to live market conditions.

John Murphy’s Ten Laws of Technical Trading

Popular author, columnist, and speaker on technical analysis, John Murphy outlines his top recommendations for beginners in this simple, but to the point essay on trend following.

These points are based on questions and comments he has received over the years after speaking to various audiences on technical analysis. This article provides an understanding of basic concepts that apply to most of the theories employed by today’s technical analysts.

Chart Patterns

Smart Chart Reading: How to Locate a Double-Bottom Base

This article explains how a stock might appear to form a cup base, but then the pattern might just fall apart. It argues that you should leave the stock a while longer and it may form a double-bottom base instead because this pattern can lead to big gains in the market.

Charts

Reviews the different types of chart patterns, including trendlines, wedge patterns, triangle patterns, channel patterns, double top, multiple top, double bottom, multiple bottom, and head and shoulders patterns.

Candlestick Patterns

An Investor’s Guide to Candlestick Patterns

This comprehensive set of articles guides a beginner investor through the definition of candlestick patterns, the different types, understanding certain patterns, and how to use them in analysis. The series is accompanied by charts and graphs and is broken up into 10 sets of mini articles that flow down the page.

10 Price Action Candlestick Patterns You Must Know

This article illustrates the 10 most important candlestick patterns to know including Doji, Harami, Engulfing, Hammer / Hanging Man among other. A great reference for active traders.

Technical Indicators

Oscillators

Multi Time Frame Analysis with Oscillators—Simple, Effective

This is a short piece on the multi time frame (MTF) analysis. Highlights that the underlying idea is to go with the larger theme on the higher time frame, and then to drill down to the lower time frame in order to gain impeccable timing and to increase the risk:reward ratio.

Argues that the best way to use MTF is through oscillators.

Moving Averages

Argues that the moving average is one of the most flexible as well as most-commonly used technical analysis indicators due to its simplicity. Includes an introduction, purpose and use, trading signals, how to use, pros and cons, and types explanation section. It mentions simple moving average (SMA) and the exponentially weighted moving average (EMA, EWMA).

Best Day Trading Chart Indicators

This article argues that a beginner trader shouldn’t just search for the best trading indicators, but the best one for you. Outlines that the three components in finding the best one are: the right time frame, the right on-chart indicators, and the set of the right off-chart indicators.

Technical Analysis Tools

Reviews basic technical analysis, how to pick a stock, stock trends, moving averages, relative strength index (RSI), Moving Average Convergence Divergence (MACD), Fibonacci Retracement, and support and resistance.

Volatility Indicators

How to Measure Volatility

Defines volatility as the “measurement of price variation over a specified period of time.” And explains how traders can use historical price movements to get an idea for what may happen in the future. The article argues that the key component of this type of probabilistic approach is the ability to see the “big picture” or general condition of the market being traded. It reviews average true range (ATR) for measuring risk.

Volatility-Based Indicators

Includes a general introduction, purpose and use, trading signals, pros and cons, and types of indicators section in explaining volatility. This includes parabolic SAR, Rate of Change (ROC), and Bollinger bands,

Breadth Indicators

5 Stock Market Breadth Indicators to Make You a Better Market Timer

Outlines what a market breadth indicator is and how they show how the “market of stocks” is doing instead of the stock market. It explains how breadth indicators can often give information about a market move before it happens. The articles gives five examples including TICK charts, McClellan oscillator, NYSE A/D Line, cumulative A/D line, and SPXA50R.

Everything You Need to Know about Market Breadth Indicators

Defines market breadth as a “technical analysis technique that compares the number of advancing securities within an index or market to the number of declining securities.” The article outlines the advance-decline index, bullish percent index, and new highs-lows index.

Volume Indicators

Volume-Based Indicators

Argues that the information yielded by volume-based indicators is most valuable during the last stages of a trend. Includes an introduction, purpose and use, pros and cons, and types section. Outlines the Money Flow Index (MFI) and the Force Index.

Technical Analysis Opinion Articles

Why Some Technical Analysis May Not Be Effective: An Interview with Michael Harris

This article is an interview with Price Action Lab Blogger and author who famously argues that “everything you know about technical analysis is wrong.” He explains that methods of the old school were not conceived when computers were available, so they had to be visual based on charts. He believes pattern were invented and are no longer relevant.

An Introduction to Graph Theory and Network Analysis (with Python codes)

Introduction

“A picture speaks a thousand words” is one of the most commonly used phrases. But a graph speaks so much more than that. A visual representation of data, in the form of graphs, helps us gain actionable insights and make better data driven decisions based on them.

But to truly understand what graphs are and why they are used, we will need to understand a concept known as Graph Theory. Understanding this concept makes us better programmers (and better data science professionals!).

But if you have tried to understand this concept before, you’ll have come across tons of formulae and dry theoretical concepts. That is why we decided to write this blog post. We have explained the concepts and then provided illustrations so you can follow along and intuitively understand how the functions are performing. This is a detailed post, because we believe that providing a proper explanation of this concept is a much preferred option over succinct definitions.

In this article, we will look at what graphs are, their applications and a bit of history about them. We’ll also cover some Graph Theory concepts and then take up a case study using python to cement our understanding.

Ready? Let’s dive into it.

Table of Contents

  • Graphs and their applications
  • History and why graphs?
  • Terminologies you need to know
  • Graph Theory Concepts
  • Getting familiar with Graphs in python
  • Analysis on a dataset

Graphs and their applications

Let us look at a simple graph to understand the concept. Look at the image below –

Consider that this graph represents the places in a city that people generally visit, and the path that was followed by a visitor of that city. Let us consider V as the places and E as the path to travel from one place to another.

The edge (u,v) is the same as the edge (v,u) – They are unordered pairs.

Concretely – Graphs are mathematical structures used to study pairwise relationships between objects and entities. It is a branch of Discrete Mathematics and has found multiple applications in Computer Science, Chemistry, Linguistics, Operations Research, Sociology etc.

The Data Science and Analytics field has also used Graphs to model various structures and problems. As a Data Scientist, you should be able to solve problems in an efficient manner and Graphs provide a mechanism to do that in cases where the data is arranged in a specific way.

  • A Graph is a pair of sets. G = (V,E) . V is the set of vertices. E is a set of edges. E is made up of pairs of elements from V (unordered pair)
  • A DiGraph is also a pair of sets. D = (V,A) . V is the set of vertices. A is the set of arcs. A is made up of pairs of elements from V (ordered pair)

In the case of digraphs, there is a distinction between `(u,v)` and `(v,u)`. Usually the edges are called arcs in such cases to indicate a notion of direction.

There are packages that exist in R and Python to analyze data using Graph theory concepts. In this article we will be briefly looking at some of the concepts and analyze a dataset using Networkx Python package.

From the above examples it is clear that the applications of Graphs in Data Analytics are numerous and vast. Let us look at a few use cases:

  • Marketing Analytics – Graphs can be used to figure out the most influential people in a Social Network. Advertisers and Marketers can estimate the biggest bang for the marketing buck by routing their message through the most influential people in a Social Network
  • Banking Transactions – Graphs can be used to find unusual patterns helping in mitigating Fraudulent transactions. There have been examples where Terrorist activity has been detected by analyzing the flow of money across interconnected Banking networks
  • Supply Chain – Graphs help in identifying optimum routes for your delivery trucks and in identifying locations for warehouses and delivery centres
  • Pharma – Pharma companies can optimize the routes of the salesman using Graph theory. This helps in cutting costs and reducing the travel time for salesman
  • Telecom – Telecom companies typically use Graphs (Voronoi diagrams) to understand the quantity and location of Cell towers to ensure maximum coverage

History and Why Graphs?

History of Graphs

If you want to know more on how the ideas from graph has been formlated – read on!

The origin of the theory can be traced back to the Konigsberg bridge problem (circa 1730s). The problem asks if the seven bridges in the city of Konigsberg can be traversed under the following constraints

  • no doubling back
  • you end at the same place you started

This is the same as asking if the multigraph of 4 nodes and 7 edges has an Eulerian cycle (An Eulerian cycle is an Eulerian path that starts and ends on the same Vertex. And an Eulerian path is a path in a Graph that traverses each edge exactly once. More Terminology is given below). This problem led to the concept of Eulerian Graph. In the case of the Konigsberg bridge problem the answer is no and it was first answered by (you guessed it) Euler.

In 1840, A.F Mobius gave the idea of complete graph and bipartite graph and Kuratowski proved that they are planar by means of recreational problems. The concept of tree, (a connected graph without cycles) was implemented by Gustav Kirchhoff in 1845, and he employed graph theoretical ideas in the calculation of currents in electrical networks or circuits.

In 1852, Thomas Gutherie found the famous four color problem. Then in 1856, Thomas. P. Kirkman and William R.Hamilton studied cycles on polyhydra and invented the concept called Hamiltonian graph by studying trips that visited certain sites exactly once. In 1913, H.Dudeney mentioned a puzzle problem. Eventhough the four color problem was invented it was solved only after a century by Kenneth Appel and Wolfgang Haken. This time is considered as the birth of Graph Theory.

Caley studied particular analytical forms from differential calculus to study the trees. This had many implications in theoretical chemistry. This lead to the invention of enumerative graph theory. Any how the term “Graph” was introduced by Sylvester in 1878 where he drew an analogy between “Quantic invariants” and covariants of algebra and molecular diagrams.

In 1941, Ramsey worked on colorations which lead to the identification of another branch of graph theory called extremel graph theory. In 1969, the four color problem was solved using computers by Heinrich. The study of asymptotic graph connectivity gave rise to random graph theory. The histories of Graph Theory and Topology are also closely related. They share many common concepts and theorems.

Why Graphs?

Here are a few points that help you motivate to use graphs in your day-to-day data science problems –

  1. Graphs provide a better way of dealing with abstract concepts like relationships and interactions. They also offer an intuitively visual way of thinking about these concepts. Graphs also form a natural basis for analyzing relationships in a Social context
  2. Graph Databases have become common computational tools and alternatives to SQL and NoSQL databases
  3. Graphs are used to model analytics workflows in the form of DAGs (Directed acyclic graphs)
  4. Some Neural Network Frameworks also use DAGs to model the various operations in different layers
  5. Graph Theory concepts are used to study and model Social Networks, Fraud patterns, Power consumption patterns, Virality and Influence in Social Media. Social Network Analysis (SNA) is probably the best known application of Graph Theory for Data Science
  6. It is used in Clustering algorithms – Specifically K-Means
  7. System Dynamics also uses some Graph Theory concepts – Specifically loops
  8. Path Optimization is a subset of the Optimization problem that also uses Graph concepts
  9. From a Computer Science perspective – Graphs offer computational efficiency. The Big O complexity for some algorithms is better for data arranged in the form of Graphs (compared to tabular data)

Terminology you should know

Before you go any further into the article, it is recommended that you should get familiar with these terminologies.

  1. The vertices u and v are called the end vertices of the edge (u,v)
  2. If two edges have the same end vertices they are Parallel
  3. An edge of the form (v,v) is a loop
  4. A Graph is simple if it has no parallel edges and loops
  5. A Graph is said to be Empty if it has no edges. Meaning E is empty
  6. A Graph is a Null Graph if it has no vertices. Meaning V and E is empty
  7. A Graph with only 1 Vertex is a Trivial graph
  8. Edges are Adjacent if they have a common vertex. Vertices are Adjacent if they have a common edge
  9. The degree of the vertex v , written as d(v) , is the number of edges with v as an end vertex. By convention, we count a loop twice and parallel edges contribute separately
  10. Isolated Vertices are vertices with degree 1. d(1) vertices are isolated
  11. A Graph is Complete if its edge set contains every possible edge between ALL of the vertices
  12. A Walk in a Graph G = (V,E) is a finite, alternating sequence of the form V i E i “> V i E i ViEi consisting of vertices and edges of the graph G
  13. A Walk is Open if the initial and final vertices are different. A Walk is Closed if the initial and final vertices are the same
  14. A Walk is a Trail if ANY edge is traversed atmost once
  15. A Trail is a Path if ANY vertex is traversed atmost once (Except for a closed walk)
  16. A Closed Path is a Circuit – Analogous to electrical circuits

Graph Theory concepts

In this section, we’ll look at some of the concepts useful for Data Analysis (in no particular order). Please note that there are a lot more concepts that require a depth which is out of scope of this article. So let’s get into it.

Average Path Length

The average of the shortest path lengths for all possible node pairs. Gives a measure of ‘tightness’ of the Graph and can be used to understand how quickly/easily something flows in this Network.

BFS and DFS

Breadth first search and Depth first search are two different algorithms used to search for Nodes in a Graph. They are typically used to figure out if we can reach a Node from a given Node. This is also known as Graph Traversal

The aim of the BFS is to traverse the Graph as close as possible to the root Node, while the DFS algorithm aims to move as far as possible away from the root node.

Centrality

One of the most widely used and important conceptual tools for analysing networks. Centrality aims to find the most important nodes in a network. There may be different notions of “important” and hence there are many centrality measures. Centrality measures themselves have a form of classification (or Types of centrality measures). There are measures that are characterized by flow along the edges and those that are characterized by Walk Structure.

Some of the most commonly used ones are:

  1. Degree Centrality – The first and conceptually the simplest Centrality definition. This is the number of edges connected to a node. In the case of a directed graph, we can have 2 degree centrality measures. Inflow and Outflow Centrality
  2. Closeness Centrality – Of a node is the average length of the shortest path from the node to all other nodes
  3. Betweenness Centrality – Number of times a node is present in the shortest path between 2 other nodes

These centrality measures have variants and the definitions can be implemented using various algorithms. All in all, this means a large number of definitions and algorithms.

Network Density

A measure of how many edges a Graph has. The actual definition will vary depending on type of Graph and the context in which the question is asked. For a complete undirected Graph the Density is 1, while it is 0 for an empty Graph. Graph Density can be greater than 1 in some situations (involving loops).

Graph Randomizations

While the definitions of some Graph metrics maybe easy to calculate, it is not easy to understand their relative importance. We use Network/Graph Randomizations in such cases. We calculate the metric for the Graph at hand and for another similar Graph that is randomly generated. This similarity can for example be the same number of density and nodes. Typically we generate a 1000 similar random graphs and calculate the Graph metric for each of them and then compare it with the same metric for the Graph at hand to arrive at some notion of a benchmark.

In Data Science when trying to make a claim about a Graph it helps if it is contrasted with some randomly generated Graphs.

Getting Familiar with Graphs in python

We will be using the networkx package in Python. It can be installed in the Root environment of Anaconda (if you are using the Anaconda distribution of Python). You can also pip install it.

Let us look at some common things that can be done with the Networkx package. These include importing and creating a Graph and ways to visualize it.

Graph Creation

Node and Edge attributes can be added along with the creation of Nodes and Edges by passing a tuple containing node and attribute dict.

In addition to constructing graphs node-by-node or edge-by-edge, they can also be generated by applying classic graph operations, such as:

Separate classes exist for different types of Graphs. For example the nx.DiGraph() class allows you to create a Directed Graph. Specific graphs containing paths can be created directly using a single method. For a full list of Graph creation methods please refer to the full documentation. Link is given at the end of the article.

Accessing edges and nodes

Nodes and Edges can be accessed together using the G.nodes() and G.edges() methods. Individual nodes and edges can be accessed using the bracket/subscript notation.

EdgeView([(1, 2), (1, 3), (2, 3)])

Graph Visualization

Networkx provides basic functionality for visualizing graphs, but its main goal is to enable graph analysis rather than perform graph visualization. Graph visualization is hard and we will have to use specific tools dedicated for this task. Matplotlib offers some convenience functions. But GraphViz is probably the best tool for us as it offers a Python interface in the form of PyGraphViz (link to documentation below).

You will first have to Install Graphviz from the website (link below). And then pip install pygraphviz –install-option=” <> . In the install options you will have to provide the path to the Graphviz lib and include folders.

PyGraphviz provides great control over the individual attributes of the edges and nodes. We can get very beautiful visualizations using it.

Usually, visualization is thought of as a separate task from Graph analysis. A graph once analyzed is exported as a Dotfile. This Dotfile is then visualized separately to illustrate a specific point we are trying to make.

Analysis on a Dataset

We will be looking to take a generic dataset (not one that is specifically intended to be used for Graphs) and do some manipulation (in pandas) so that it can be ingested into a Graph in the form of a edgelist. And edgelist is a list of tuples that contain the vertices defining every edge

The dataset we will be looking at comes from the Airlines Industry. It has some basic information on the Airline routes. There is a Source of a journey and a destination. There are also a few columns indicating arrival and departure times for each journey. As you can imagine this dataset lends itself beautifully to be analysed as a Graph. Imagine a few cities (nodes) connected by airline routes (edges). If you are an airline carrier, you can then proceed to ask a few questions like

  1. What is the shortest way to get from A to B? In terms of distance and in terms of time
  2. Is there a way to go from C to D?
  3. Which airports have the heaviest traffic?
  4. Which airport in “in between” most other airports? So that it can be converted into a local hub
  1. We notice that origin and destination look like good choices for Nodes. Everything can then be imagined as either node or edge attributes. A single edge can be thought of as a journey. And such a journey will have various times, a flight number, an airplane tail number etc associated with it
  2. We notice that the year, month, day and time information is spread over many columns. We want to create one datetime column containing all of this information. We also need to keep scheduled and actual time of arrival and departure separate. So we should finally have 4 datetime columns (Scheduled and actual times of arrival and departure)
  3. Additionally, the time columns are not in a proper format. 4:30 pm is represented as 1630 instead of 16:30. There is no delimiter to split that column. One approach is to use pandas string methods and regular expressions
  4. We should also note that sched_dep_time and sched_arr_time are int64 dtype and dep_time and arr_time are float64 dtype
  5. An additional complication is NaN values

We now have time columns in the format we wanted. Finally we may want to combine the year , month and day columns into a date column. This is not an absolutely necessary step. But we can easily obtain the year, month and day (and other) information once it is converted into datetime format.

Now import the dataset using the networkx function that ingests a pandas dataframe directly. Just like Graph creation there are multiple ways Data can be ingested into a Graph from multiple formats.

As is obvious from looking at the Graph visualization (way above) – There are multiple paths from some airports to others. Let us say we want to calculate the shortest possible route between 2 such airports. Right off the bat we can think of a couple of ways of doing it

  1. There is the shortest path by distance
  2. There is the shortest path by flight time

What we can do is to calculate the shortest path algorithm by weighing the paths with either the distance or airtime. Please note that this is an approximate solution – The actual problem to solve is to calculate the shortest path factoring in the availability of a flight when you reach your transfer airport + wait time for the transfer. This is a more complete approach and this is how humans normally plan their travel. For the purposes of this article we will just assume that is flight is readily available when you reach an airport and calculate the shortest path using the airtime as the weight

Let us take the example of JAX and DFW airports:

Conclusion

This article has at best only managed a superficial introduction to the very interesting field of Graph Theory and Network analysis. Knowledge of the theory and the Python packages will add a valuable toolset to any Data Scientist’s arsenal. For the dataset used above, a series of other questions can be asked like:

  1. Find the shortest path between two airports given Cost, Airtime and Availability?
  2. You are an airline carrier and you have a fleet of airplanes. You have an idea of the demand available for your flights. Given that you have permission to operate 2 more airplanes (or add 2 airplanes to your fleet) which routes will you operate them on to maximize profitability?
  3. Can you rearrange the flights and schedules to optimize a certain parameter (like Timeliness or Profitability etc)

If you do solve them, let us know in the comments below!

Network Analysis will help in solving some common data science problems and visualizing them at a much grander scale and abstraction. Please leave a comment if you would like to know more about anything else in particular.

Bibiliography and References

About the Author

Srivatsa currently works for TheMathCompany and has over 7.5 years of experience in Decision Sciences and Analytics. He has grown, led & scaled global teams across functions, industries & geographies. He has led India Delivery for a cross industry portfolio totalling $10M in revenues. He has also conducted several client workshops and training sessions to help level up technical and business domain knowledge.

During his career span, he has led premium client engagements with Industry leaders in Technology, e-commerce and retail. He helped set up the Analytics Center of Excellence for one of the world’s largest Insurance companies.

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