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A Python-Based Financial Modeling Program

Ultimate Python for Finance   [No Prior Experience Required] | Intermediate | Advanced

Our program offers hands-on training to give you the skills and experience you need to succeed in the field of python-based financial modeling. You'll learn how to write algorithms, create simulators, develop predictive engines, and backtest investment and risk models, trading strategies, and a lot more. With our comprehensive program, you'll gain the practical knowledge and experience necessary to become proficient in using python in the field of financial modeling.

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HOURS ON-DEMAND

PYTHON-BASED MODELS

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4.9

PYTHON PROJECTS

BLOGS PUBLISHED

RATING ACHIEVED

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Master the Most Sought-After Skills in Finance and Advance Your Career

Are you ready to become a professional in the field of financial modeling? Our program is here to help you achieve your goals! Through hands-on experience, you'll learn how to build financial models that are used in finance and other industries.

 

You'll learn how to write algorithms and create simulators fo financial investment and risk models, develop predictive engines, formulate and implement trading strategies, and a lot more. You'll also learn how to backtest financial investment and risk models to ensure their effectiveness. But that's not all - this program will also help you develop the mindset of a programmer and the approach of an engineer. You'll learn how to think logically and systematically, and you'll gain a strong foundation in the principles of model building.

 

By the end of the program, you'll have the skills and knowledge needed to succeed as a professional in the field of financial modeling. The practical experience you'll gain through this program is essential for anyone who wants to become proficient in using python in the field of financial modeling. Whether you're a finance professional looking to learn python programming or a programmer who wants to learn more about finance, this program will provide you with the knowledge and experience you need to succeed.

 

With a strong foundation in python-based financial modeling, you'll be well-equipped to take on challenging projects and make a positive impact in your field. Don't miss out on this exciting opportunity to advance your career in financial modeling. Sign up for our program today and start your journey toward professional success!

Includes Welcome to Python Basics [ 30+ hours ] training program, plus . . .

6-months [ 100+ Hours ] training program.

Get hands-on experience on real-world projects [ 25+ Projects ] and team collaboration.

Q&A support incl. python model assistance.

Online instructor-led weekend live batch + recorded sessions available as fallback option.

Downloadable access to python-based models.

Read access to financial book, interview guides, CV/Resume preparation, and reference materials.

Get placement opportunities and a certificate on completion.

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Premium Subscription

Ultimate Python for Finance

reach out to us at contact@thefinanalytics.com

INR 25,000

USD 320

Inclusions

Welcome to Python Basics

INR 5,000

The Financial Book

INR 100

Ace Your Python Programming Interview

INR 100

Financial Derivatives Interview Handbook

INR 100

Mastering Market Risk: An Interview Prep Guide

INR 100

CV/Resume, Interviews, and Placement

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Refundable Fee Option

Opt for our Refundable Fee Option: Pay 1.5 times the subscription fee for the "Ultimate Python for Finance" program, and get a full fee refund upon successful qualification.

Qualification Criteria

✔     Pass assessment tests.

✔     Complete excel and python-based modeling projects.

✔     Score atleast 80 credits.

✔     Adhere to program requirements and guidelines.

Exclusions

✘     Q&A support incl. python model assistance.

✘     Online instructor-led weekend live batch.

✘     CV/Resume, Interviews, and Placement.

✘     Get placement opportunities and a certificate on completion.

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Corporate Training

strengthen, reinforce, and enhance your team's skills.

empowering success through strengthening, reinforcing, and enhancing the skills with our ultimate python for finance program.

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Our Expert-Led Resources For Your Journey

Unleash your full potential with expert-led resources that focus on practical understanding by taking advantage of our step-by-step self-paced materials to learn and practice at your own pace.

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Ace Your Python Programming Interview

Notifications

Your Ultimate Roadmap for Python Basics

It's about setting up your development environment. You'll familiarize yourself with Anaconda Navigator and Jupyter..

Expected: 29th Sept. 2023

Test Your Skills | Batch 3.7 & 3.8

Challenge Yourself: Prove Your Python Programming Skills with Our 25-Question Assessment

Results: Available

Test Your Skills | Batch 3.7 & 3.8

Push Your Limits: Prove Your Python Programming Skills with Our 15-Question Assessment

Submission Date: 17th March 2023

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Program Coverage Curated By Experienced Mentors

We're focused on delivering practical skills to finance & risk professionals with an in-depth understanding & implementation using python. We never stop adding more content to it.

Introduction to Python Programming

→ Introducing - Strings | Integers | Floats | Variables | Indentation | Comments

→ Operators - Arithmetic | Assignment | Logical | Comparison | Identity | Bitwise

→ Libraries - Pandas | NumPy | SciPy | Math | Datetime | Matplotlib | yFinance | NSEPy, etc.

→ Write your first Python code

→ Python - DataFrame(s) | Dictionary

→ Indexing | Location | Index Location

→ Input & Output Sections

Introduction to Python Programming [7 Sessions]

Python Statements

→ List | Tuple | Set - Creating | Indexing | Modifying - Adding/Appending | Removing | Updating | Reversing | Sorting | Casting | Deleting | Extensions

→ List of Lists | Tuple of Tuples | List of Tuples | Tuple of Lists | List of Sets, Tuple of Sets, etc.

→ List Comprehensions

→ Dynamic Statements - if | elif | else | for loop | while loop

→ Exception Handling in Python - try | except | finally

→ Other Statements - return | pass | break

Python Functions & Methods

→ Object Oriented Programming (OOP) Concepts - Class | Objects | Inheritance - Parent & Child Class | Child & Child Class | Polymorphism | Encapsulation

→ Defining & Calling - Class | Functions | Methods | Parameters/Arguments | Variables/Attributes

→ Other Functions - print | lambda 

→ Python Project: Bank Account System: An Object-Oriented Finance Project

Building Blocks – Python for Finance

→ Extract Company Information → Extract Historical & Intraday Data - Stocks | Indices → Extract Corporate Events - Dividends | Splits → Fetching Real-time Prices - Stocks | Indices | Cryptocurrencies

→ Computation of Historical Time-Series Market Shocks - Absolute | Proportional - Discrete | Continuous

→ Extract TimeSeries of Options for Multiple Strikes for Multiple Expiries & Prepare Options Chain

→ Generating Random Numbers to be used for Estimation Engines → Read & Export Excel Files

→ Dynamics of Extracting Data - Multiple Stocks | Multiple Indices | Multiple Options with Multiple Strikes & Expiries - Call Option | Put Option

→ Python Project: Building a Stock Screening and Ranking Tool

Big Data Analytics

→ Libraries - Pandas - Data Series - Part 1 | 2 | DataFrames - Part 1 | 2 | 3 | Numpy - Part 1 | 2

→ Data Briefing & Data Control - Shape | Information | Describe | Datatypes

→ Data Cleaning - Handling #N/A | Handling 0 | Handling Missing Values

→ Data Manipulation - Creating | Sorting | Filtering | Merging | Splitting | Renaming | Relocating - Rows | Columns

→ Data Import/Export - Python from/to Excel

→ Data Indexing - Location | Index Location - Rows | Columns

→ Data Profiling & Pivoting Data - Values | Index | Columns → Data Visualization

Data Visualization for Data Science

→ Installation of Data Visualization Library

→ Plotting Charts & Sub-Charts - Line | Histogram | Scatter | Pie | Bar | Box | Heatmaps | Conditional Formatting

→ Multiple SubPlots | Images | Three-Dimensional

→ Customizing Plots - Title | Axis Labels & Ticks | Data Labels | Legends | Resizing | Font Style | Font Color | Alpha | Line Style | Alignments | Colors | Styles | Markers

→ Handling Missing Data | Performing Calculations | Reassigning Values | Adding Annotations

→ Highlighting Data Points/Results & Labels | Plotting Time-Series Data | Continuous & Categorical Data

Statistical & Quantitative Techniques

→ Statistics Data Analysis - Mean - Arithmetic & Geometric | Median | Mode | Range | Variance | Absolute Deviation | Standard Deviation | Skewness | Kurtosis | Covariance | Correlation | Slope

→ Sampling | Sampling Distribution | Central Limit Theorem in Action

→ Probability Distribution [Probability Mass Function | Probability Density Function | Cumulative Distribution Function | Percent Point Function] - Uniform | Normal | Log-Normal | Binomial | Gamma | Exponential | Poisson | Bernoulli

→ Time Series Analysis | Regression Analysis - Auto-Regression | Simple & Multi Linear Regression 

→ Simulation Methods & Stochastic Processes

Investment Portfolio Construction & Optimization

→ Risk (Standard Deviation) & Return (Mean) Profile - Stocks | Portfolio | Benchmark

→ Variance-Covariance & Correlation Matrices

→ Portfolio Construction & Optimization using - Equal Weights | Random Weights | Passive Weights (Benchmark Index) | Simulated Weights | Optimizer Engine

→ Security Selection | Asset Allocation - Strategic | Tactical vs. Benchmark Index

→ Replicating Benchmark Index (Passive Fund) & Active-Passive Asset Allocation Mix

→ Optimal Portfolio - Weights | Quantities using Latest Prices & Unallocated Funds

→ Signals to Churn Existing Portfolio - Buy-Sell Quantities | Rebalancing Decision

Investment Performance Monitoring & Evaluation

→ Beta - Individual Stock | Portfolio → Risk-Return Profile - Individual Stocks vs. Portfolio vs. Benchmark

→ Risk-Adjusted Return from Capital Asset Pricing Model (CAPM)

→ Active Risk (Tracking Error) and Active Return (Excess Return)

→ Performance - Sharpe Ratio | Sortino Ratio | Treynor Ratio | Information Ratio | Diversification Ratio | CAGR | Maximum Drawdown | Calmar Ratio

→ Optimal Portfolio vs. Replicated Benchmark Portfolio (Passive Fund)

→ Analytics - Individual Stocks | Optimal Portfolio | Benchmark Index

Risk Measurement Models | Model Development & Validation

→ Value-at-Risk (VaR) Models - Historical Simulation Method | Monte-Carlo Simulation Method → Value-at-Risk (VaR) Models - Parametric Method 

→ Downside Risk | Tail Risk | Conditional Value-at-Risk (CVaR | Expected Shortfall) 

→ Stressed Value-at-Risk (SVaR), Incremental & Marginal Value-at-Risk (IVaR & MVaR)

→ Scenario Analysis | Stress Testing - Scenario Creation for Identified Shocks - Spot Shocks [Positive | Negative | Antithetic] - Equity, Rates, FX, Commodity | Volatility Shocks - Normal/Local | Log-Normal/ATM Implied | Full Revaluation Model | Scenario Profit & Loss | Risk - EQ, IR, FX, COM | Cross Risk

→ Model Validation through Backtesting | Traffic Light Approach | Breaches in Position Risk Flags/Limits

Pricing & Valuation Models | Model Development & Validation

→ Interest-Rate Bonds, IR Swaps - Discounting Cash Flow Model → Equity Futures, Forwards, FRAs - Cost of Carry Model → Equity, Rates, FX Option Derivatives - Binomial Model (Single-Period | Two-Period | Multi-Period) - Risk-Neutralization Approach | Delta-Hedging Approach | Replicating Portfolio Approach | Black-Scholes-Merton Option Pricing Model | Monte-Carlo Simulation - Point Estimation | Path Estimation

→ Full Revaluation vs. Partial Revaluation - Taylor Series Expansion/Approximation | Ladder-Based Interpolation Technique → Testing Option's Price under Put-Call Parity Theorem

→ Model Validation - Challenging Assumptions - Distributional, Constant Volatility, Interest Rate | Model Inputs & Model Formula

Option Greeks & Hedging Techniques

→ Risk Sensitivities - Duration | DV01 | Credit Spread CS01 | Delta | Gamma | Vega | Theta | Rho | Vanna | Volga

→ Impact on Price & Greeks due to - Moneyness | Volatility | Time-Decay [Incl. ITM | ATM | OTM]

→ Hedging Tool - Static Hedging | Dynamic Hedging | Rebalancing Portfolio & Frequency

→ Delta Hedging & Rebalancing to Remain Delta-Neutral → Gamma Hedging & Rebalancing to Remain Gamma-Neutral → Vega Hedging & Rebalancing to Remain Vega-Neutral → Delta-Gamma Hedging & Rebalancing to Remain Delta-Gamma-Neutral → Delta-Vega Hedging & Rebalancing to Remain Delta-Vega-Neutral

Predictive & Volatility Engines | Modeling Interest Rates

→ Price Prediction Engines - Monte-Carlo Simulation | Long Short-Term Memory with Machine Learning Algorithms → Volatility Engines - Implied Volatility | Exponentially Weighted Moving Average | Generalized AutoRegressive Conditional Heteroskedasticity (1,1) → Normal & Log-Normal Volatilities

→ US Treasury Rates & Yield Curve - Spot Rates | Forward Rates | Zero Rates | Par Rates | Historical Time-Series of Interest Rates & Interest Rate Shocks - Absolute | Proportional - Discrete | Continuous |

→ Yield Curve Construction - Linear Interpolation & Extrapolation | Bootstrapping | OLS Method for Interest Rates Modeling - Linear | Polynomial - Quadratic | Cubic Spline Piecewise Segmentation | Quartic | Nelson-Siegel Model | Nelson-Siegel-Svensson Model

Technical Indicators & Strategies

→ Relative Strength Index (RSI) | Simple Moving Average (SMA) | Exponential Moving Average (EMA) | Stochastic Oscillator | Bollinger Bands | Moving Average Convergence & Divergence (MACD)

→ Trading Strategy - Automating | Backtesting | Simulating Backtest Period

→ Identifying Best Entry & Exit Points under a Trading Strategy → Generating Market Signals & Trade Orders

→ Support & Resistance Level with Pivot Points - Cash Market | Option Market

→ Option Strategies - Long & Short Straddle | Long & Short Strangle

→ Open Interest & Change in Open Interest | Positional & Intraday Sentiments | Put-Call Ratio (PCR) using Option Chain - Stock | Index

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