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Standard Normal Distribution and Z-Score With Python On Live Data | 56/100 Days of Python Algo Trad.
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Standard Normal Distribution and Z-Score With Python On Live Data | 56/100 Days of Python Algo Trading
In Day 56 of "100 Days of Python Algo Trading," we delve into the practical applications of the standard normal distribution and Z-scores using Python, with a focus on live stock data. This session will cover the theoretical aspects of standardization, the benefits and drawbacks of this approach, and hands-on examples of data analysis including fetching, plotting, and statistical calculations for stock data.
What You'll Learn:
Introduction (Starts at 00:00:00): Quick overview of today's session and its importance in financial analytics and trading strategies.
Plan of Action (Starts at 00:00:32): Outline of the steps we'll take during the tutorial, from data fetching to detailed statistical analysis.
Revision of the Standard Normal Distribution (Starts at 00:01:20): A brief recap of what the standard normal distribution is and why it is pivotal in statistical analysis, particularly in the context of financial data.
Pros and Cons of Standardizing Data (Starts at 00:03:15): Discussion on the advantages and limitations of transforming data into a standard normal format, helping traders understand when and why to use this method.
Stock Data Analysis: Fetching 3 Years, Histogram Plotting, Basic Parameters, and CDF Calculation (Starts at 00:08:12): Step-by-step demonstration on how to:
Fetch three years of stock data using Python.
Plot histograms to visualize the distribution of stock prices.
Calculate basic statistical parameters (mean, median, standard deviation).
Compute and plot the cumulative distribution function (CDF) for further insights into the data’s distribution.
Engage with Us:
Interactive Tutorial: Follow along with the provided Python code to apply these concepts to live stock data.
Ask Questions: Unsure about a concept or need clarification on a Python function? Leave a comment below, and we’ll help out.
Subscribe for More: Keep up with our series to enhance your skills in algorithmic trading by subscribing and activating notifications.
Join us as we break down complex statistical concepts into practical tools that can be applied directly to enhance your trading decisions and risk management strategies. Whether you're just starting out or looking to deepen your understanding of financial data analysis, this session will provide valuable insights and hands-on skills.
Timestamps:
00:00:00 - Introduction
00:00:32 - Plan of Action
00:01:20 - Revision of the Standard Normal Distribution
00:03:15 - Pros and Cons of Standardizing Data
00:08:12 - Stock Data Analysis: Fetching, Histogram Plotting, Basic Parameters, and CDF Calculation
This session is designed to equip you with the knowledge to transform theoretical statistical concepts into actionable insights using Python, helping you navigate the complexities of financial markets with greater confidence.
Connect with us
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Facebook
X ( twitter )
LinkedIn
Discord
Telegram
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Tags
python for beginners
python for finance
python for trading
python algo trading tutorial
AI in trading
machine learning for beginners
machine learning algorithms for trading
deep learning for finance
algorithmic trading strategies
how to build a trading bot with python
trading bot tutorial
algo trading for beginners
machine learning for beginners course
AI for beginners course
python for algo trading beginners
machine learning for algo trading strategies
how to build an AI trading bot with python
Python mini projects
Data science projects
Data analysis projects
Algotrading projects
#standardnormaldistribution #normaldistribution #yfinance #livedata #livetradedata #trading #optionstraining #stockoptions #stock #python #pythonprogramming #pythontrading #pythonalgotrading #artificialintelligence #AI #machinelearning #ML #deeplearning #algorithms #algotrading #algorithmictrading #trading #finance #cryptocurrency #bitcoin #option #optiontrading #optionvolatility #optiontrading #trading #tradingstrategies #quantitativetrading #quantitativeanalysis
In Day 56 of "100 Days of Python Algo Trading," we delve into the practical applications of the standard normal distribution and Z-scores using Python, with a focus on live stock data. This session will cover the theoretical aspects of standardization, the benefits and drawbacks of this approach, and hands-on examples of data analysis including fetching, plotting, and statistical calculations for stock data.
What You'll Learn:
Introduction (Starts at 00:00:00): Quick overview of today's session and its importance in financial analytics and trading strategies.
Plan of Action (Starts at 00:00:32): Outline of the steps we'll take during the tutorial, from data fetching to detailed statistical analysis.
Revision of the Standard Normal Distribution (Starts at 00:01:20): A brief recap of what the standard normal distribution is and why it is pivotal in statistical analysis, particularly in the context of financial data.
Pros and Cons of Standardizing Data (Starts at 00:03:15): Discussion on the advantages and limitations of transforming data into a standard normal format, helping traders understand when and why to use this method.
Stock Data Analysis: Fetching 3 Years, Histogram Plotting, Basic Parameters, and CDF Calculation (Starts at 00:08:12): Step-by-step demonstration on how to:
Fetch three years of stock data using Python.
Plot histograms to visualize the distribution of stock prices.
Calculate basic statistical parameters (mean, median, standard deviation).
Compute and plot the cumulative distribution function (CDF) for further insights into the data’s distribution.
Engage with Us:
Interactive Tutorial: Follow along with the provided Python code to apply these concepts to live stock data.
Ask Questions: Unsure about a concept or need clarification on a Python function? Leave a comment below, and we’ll help out.
Subscribe for More: Keep up with our series to enhance your skills in algorithmic trading by subscribing and activating notifications.
Join us as we break down complex statistical concepts into practical tools that can be applied directly to enhance your trading decisions and risk management strategies. Whether you're just starting out or looking to deepen your understanding of financial data analysis, this session will provide valuable insights and hands-on skills.
Timestamps:
00:00:00 - Introduction
00:00:32 - Plan of Action
00:01:20 - Revision of the Standard Normal Distribution
00:03:15 - Pros and Cons of Standardizing Data
00:08:12 - Stock Data Analysis: Fetching, Histogram Plotting, Basic Parameters, and CDF Calculation
This session is designed to equip you with the knowledge to transform theoretical statistical concepts into actionable insights using Python, helping you navigate the complexities of financial markets with greater confidence.
Connect with us
YouTube
X ( twitter )
Discord
Telegram
GitHub
Tags
python for beginners
python for finance
python for trading
python algo trading tutorial
AI in trading
machine learning for beginners
machine learning algorithms for trading
deep learning for finance
algorithmic trading strategies
how to build a trading bot with python
trading bot tutorial
algo trading for beginners
machine learning for beginners course
AI for beginners course
python for algo trading beginners
machine learning for algo trading strategies
how to build an AI trading bot with python
Python mini projects
Data science projects
Data analysis projects
Algotrading projects
#standardnormaldistribution #normaldistribution #yfinance #livedata #livetradedata #trading #optionstraining #stockoptions #stock #python #pythonprogramming #pythontrading #pythonalgotrading #artificialintelligence #AI #machinelearning #ML #deeplearning #algorithms #algotrading #algorithmictrading #trading #finance #cryptocurrency #bitcoin #option #optiontrading #optionvolatility #optiontrading #trading #tradingstrategies #quantitativetrading #quantitativeanalysis
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