Dr AI Academy

Deepak Rai an IIT Kharagpur Certified AI Expert with More than 10 Years of IT Experience

Course available for lifetime after purchase

Basic Python in Finance: How to Implement Financial Trading Strategies and Analysis using Python
Course Overview:
This course provides a foundational understanding of Python programming tailored for finance professionals and traders. It focuses on utilizing Python to analyze financial markets, build and test trading strategies, and automate financial tasks. The course covers key Python libraries, data manipulation techniques, and implementation of basic financial models.

Course Duration:
4 weeks (8 sessions; 2 sessions per week)
Each session: 1.5 hours

Module 1: Introduction to Python for Finance
Session 1: Setting up the Environment and Python Basics
Overview of Python and its applications in finance
Installing Python and Jupyter Notebook
Introduction to variables, data types, and basic operations
Control structures: loops and conditionals
Session 2: Financial Data Handling using Pandas
Introduction to the Pandas library
Importing and organizing financial datasets (CSV, Excel, APIs)
DataFrames: Manipulation and visualization of financial data
Cleaning and preparing data for analysis
Module 2: Working with Financial Data and Time Series
Session 3: Time Series Analysis in Finance
Understanding time series data and its importance in finance
Handling time-indexed data in Pandas
Resampling, shifting, and rolling window analysis
Visualizing trends, seasonality, and cycles in time series data
Session 4: Introduction to Numpy and Statistical Analysis
Introduction to the Numpy library for numerical operations
Basic statistical functions: mean, variance, correlation, etc.
Applying descriptive statistics to financial datasets
Linear regression for stock price forecasting
Module 3: Financial Trading Strategies with Python
Session 5: Moving Averages and Technical Indicators
Implementing moving averages (SMA, EMA) in Python
Calculating and plotting financial indicators (RSI, MACD)
Understanding and backtesting simple trading strategies
Identifying entry and exit signals using Python
Session 6: Risk and Performance Analysis of Strategies
Introduction to risk management in trading
Calculating performance metrics (Sharpe ratio, drawdown)
Analyzing risk using historical volatility and beta
Evaluating the effectiveness of trading strategies
Module 4: Advanced Financial Analysis and Automation
Session 7: Automating Financial Data Retrieval
Using APIs to retrieve live financial data (Yahoo Finance, Alpha Vantage)
Automating the process of data extraction and strategy testing
Building a simple stock screener using Python
Session 8: Building and Backtesting Trading Strategies
Introduction to the Backtrader library for strategy backtesting
Designing and implementing a custom trading strategy
Simulating historical trades and analyzing results
Optimizing strategies based on backtesting outcomes
Final Project: Implementing a Python-Based Trading Strategy
Build and backtest your own trading strategy using historical data
Analyze the performance and present your findings
Key Takeaways:
Hands-on experience in using Python for financial data analysis and trading strategy development
Ability to implement, backtest, and automate trading strategies using Python
Proficiency in handling financial datasets and performing quantitative analysis
Suggested Libraries:
Pandas
Numpy
Matplotlib/Seaborn (for visualization)
YFinance/Alpha Vantage (for financial data APIs)
Backtrader (for backtesting trading strategies)
This course outline provides a step-by-step guide to building a solid foundation in Python programming for finance, focusing on trading strategies and data analysis.

Python for Stock Trading [Hindi]

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Hands-On 1 with Financial Data Handling using pandas

Maharashtra

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