Financial Modelling & Valuations
By Jobaaj Learnings
₹9,999/-₹17,999/-
34,310 Enrolled Students
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Reserve Your Spot Now!Python for Finance - Technical Analysis
Use Python for technical analysis based on technical indicators. Backtest, optimize, and create TA Trading Strategies in Python
1 Hours 34 Mins
₹6,999/-₹13,999/-
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Created by
Jobaaj Learnings
Course description
By taking this course, you will be able to test your trading ideas and hypothesis and challenge them. Thousands of trading strategies can be coded and tested within minutes using Python Coding Frameworks and Templates. Decide which strategies are profitable and discard those that aren't!
- This course covers the following Technical Analysis Tools and Indicators:
- Interactive Line Charts and Candlestick Charts
- Interactive Volume Charts
- Trend, Support, and Resistance Lines
- Simple Moving Average (SMA)
- Exponential Moving Average (EMA)
- Moving Average Convergence Divergence (MACD)
- Relative Strength Index (RSI)
- Stochastic Oscillator
- Bollinger Bands
- Pivot Point (Price Action)
- Fibonacci Retracement (Price Action)
Significant Highlights of The Course
- Deep Dive in Pandas, Financial Data, and Financial Concepts
- Online video lectures
- Quizzes for Practice
- Taught by qualified Python professional
- Projects at the end of the course
What else do we offer?
- Practical-oriented course
There is a strong practical focus on the course lessons, and students work only with the existing programs to demonstrate the concepts. Every module ends with a Quiz containing practical questions related to the course, and students are encouraged to answer the questions before moving forward. - Quizzes for practice
A quiz is also included at the end of every section of this course and the module end questions. We have also provided worksheets, assignments, and projects for practice. - Instructor support for questions
Connect with Instructor via WhatsApp/Mail. Jobaaj understands that students will have questions about the course. It is also necessary for a healthy learning process, so we encourage students to ask their questions in the video discussion forum. Every question will be answered as soon as possible.
Who can take this Course?
- For Investment & Finance Professionals who are interested in transitioning from Excel to Python to boost their careers and work efficiency.
- Finance Students and Researchers who learned to handle large datasets and reached the limits of Excel.
- Data Scientists who want to improve their data handling/manipulation skills (in particular for statistical data).
- Anyone interested in data science (financial).
- Anybody who is interested in how Financial Performance is measured and how indices (stock) and portfolios are created, analyzed, visualized, and optimized. By using data examples instead of theories and formulas, it is easier to understand the concepts.
Prerequisite
-
Knowledge Of Python
-
Knowledge of Financial Concepts
Lesson List
1
Overview
What Libraries/Packages are required for TA?
00:07:41
Fetching Financial Data using Pandas DataReader
00:09:53
Line Chart of Closing Prices via Matplotlib
00:07:59
Interactive and dynamic charts using Plotly
00:07:18
Customising Plotly Charts
00:09:55
Candlestick and OHLC Bar Charts using Python
00:11:06
Customizing Bar Size / Granularity of Chart
00:06:56
Adding Volume Charts Using QuantFig
00:06:32
Adding Technical Indicators using QuantFig
00:04:18
Support Resistance and Trend Lines
00:11:23
2
Simple Moving Averages (SMA) and Introduction to Backtesting
Creating Simple Strategy - Buy and Hold
00:11:50
Performance Metrics
SMA Crossover Strategies - Overview
Defining an SMA Crossover Strategy
Vectorized Strategy Backtesting
Finding the optimal SMA Strategy
3
Exponential Moving Averages (EMA)
EMA Crossover Strategies - Overview
Getting the Data
EMA vs. SMA
Defining an EMA Crossover Strategy
Vectorized Strategy Backtesting
4
Moving Average Convergence Divergence (MACD)
MACD Strategies - Overview
Getting the Data
Defining an MACD Strategy (Part 1)
Defining an MACD Strategy (Part 2)
5
Relative Strength Index (RSI)
RSI Strategies - Overview
Getting the Data
Defining an RSI Strategy (Part 1)
Defining an RSI Strategy (Part 2)
Vectorized Strategy Backtesting
6
Working with two or many Indicators - MACD & RSI
A combined MACD / RSI Strategy - Overview
Backtesting and Optimizing the Strategies separately
Combining MACD with RSI and Backtesting
7
Stochastic Oscillator
Getting the Data
Defining an SO Strategy
Vectorized Strategy Backtesting
8
Bollinger Bands
Bollinger Bands - Overview
Getting the Data
Defining a Bollinger Bands Mean-Reversion Strategy (Part 1)
Defining a Bollinger Bands Mean-Reversion Strategy (Part 2)
Vectorized Strategy Backtesting
9
Pivot Point Strategies
Pivot Point - Overview and Data requirements
Adding Pivot Point and Support and Resistance Lines
Defining a simple Pivot Point Strategy
Vectorized Strategy Backtesting
Starting with raw Data
Preparing the Data (1) - Timezone Conversion
Preparing the Data (2) - Resampling to daily (NY Close)
Preparing the Data (3) - OHLC Resampling
Preparing the Data (4) - Merging Intraday and Daily Data
10
Fibonacci Retracement Strategies
Getting the Data
A first Intuition on Fibonacci Retracement (Uptrend)
A first Intuition on Fibonacci Retracement (Downtrend)
Identifying Local Highs
Identifying Local Lows
Highs and Lows - an iterative approach
Identifying Trends (Uptrend / Downtrend)
Adding Fibonacci Retracement Levels
A Fibonacci Retracement Breakout Strategy
Vectorized Strategy Backtesting
Final Remarks and alternative Strategies
1
Overview
What Libraries/Packages are required for TA?
00:07:41Fetching Financial Data using Pandas DataReader
00:09:53Line Chart of Closing Prices via Matplotlib
00:07:59Interactive and dynamic charts using Plotly
00:07:18Customising Plotly Charts
00:09:55Candlestick and OHLC Bar Charts using Python
00:11:06Customizing Bar Size / Granularity of Chart
00:06:56Adding Volume Charts Using QuantFig
00:06:32Adding Technical Indicators using QuantFig
00:04:18Support Resistance and Trend Lines
00:11:232
Simple Moving Averages (SMA) and Introduction to Backtesting
Creating Simple Strategy - Buy and Hold
00:11:50Performance Metrics
SMA Crossover Strategies - Overview
Defining an SMA Crossover Strategy
Vectorized Strategy Backtesting
Finding the optimal SMA Strategy
3
Exponential Moving Averages (EMA)
EMA Crossover Strategies - Overview
Getting the Data
EMA vs. SMA
Defining an EMA Crossover Strategy
Vectorized Strategy Backtesting
4
Moving Average Convergence Divergence (MACD)
MACD Strategies - Overview
Getting the Data
Defining an MACD Strategy (Part 1)
Defining an MACD Strategy (Part 2)
5
Relative Strength Index (RSI)
RSI Strategies - Overview
Getting the Data
Defining an RSI Strategy (Part 1)
Defining an RSI Strategy (Part 2)
Vectorized Strategy Backtesting
6
Working with two or many Indicators - MACD & RSI
A combined MACD / RSI Strategy - Overview
Backtesting and Optimizing the Strategies separately
Combining MACD with RSI and Backtesting
7
Stochastic Oscillator
Getting the Data
Defining an SO Strategy
Vectorized Strategy Backtesting
8
Bollinger Bands
Bollinger Bands - Overview
Getting the Data
Defining a Bollinger Bands Mean-Reversion Strategy (Part 1)
Defining a Bollinger Bands Mean-Reversion Strategy (Part 2)
Vectorized Strategy Backtesting
9
Pivot Point Strategies
Pivot Point - Overview and Data requirements
Adding Pivot Point and Support and Resistance Lines
Defining a simple Pivot Point Strategy
Vectorized Strategy Backtesting
Starting with raw Data
Preparing the Data (1) - Timezone Conversion
Preparing the Data (2) - Resampling to daily (NY Close)
Preparing the Data (3) - OHLC Resampling
Preparing the Data (4) - Merging Intraday and Daily Data
10
Fibonacci Retracement Strategies
Getting the Data
A first Intuition on Fibonacci Retracement (Uptrend)
A first Intuition on Fibonacci Retracement (Downtrend)
Identifying Local Highs
Identifying Local Lows
Highs and Lows - an iterative approach
Identifying Trends (Uptrend / Downtrend)
Adding Fibonacci Retracement Levels
A Fibonacci Retracement Breakout Strategy
Vectorized Strategy Backtesting
Final Remarks and alternative Strategies
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