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Pricing Options and Implied Volatility with Python

Python

PyScript

Data Science

Automate Your Machine Learning with PostgresML

Data Science

8 Visualizations with Python to Handle Multiple Time-Series Data

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Pricing Options and Implied Volatility with Python

Quant Finance

Pricing Options and Implied Volatility with Python

Python

PyScript

Data Science

Automate Your Machine Learning with PostgresML

8 Visualizations with Python to Handle Multiple Time-Series Data

Data Science

8 Visualizations with Python to Handle Multiple Time-Series Data

Python

Holy 🦆uck! Fast Analysis with DuckDB + Pyarrow

Data Science

Economic Data Analysis Project with Python Pandas – Data scraping, cleaning and exploration

Obtaining Time Series Datasets in Python 2022

Quant Finance

Obtaining Time Series Datasets in Python 2022

Quant Finance

Quant Finance

Pricing Options and Implied Volatility with Python

Quant Finance

Obtaining Time Series Datasets in Python 2022

Quant Finance

Analyzing stock data near events

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GS Quant

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Introduction to Atomic Simulations by Metropolis Monte Carlo

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Blankly

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Swap curve fitting in TensorFlow Quant Finance (Jupyter notebook)

Quant Finance

How To Create A Fully Automated AI Based Trading System With Python

Quant Finance

Algorithmic Trading Using Python

Trading

Automate Your Machine Learning with PostgresML

Trading

Build and Deploy Quantitative Models for stocks, crypto, and forex

Rapidly build and deploy quantitative models for stocks, crypto, and forex Blankly is a live trading engine, backtest runner and...

A Streamlit Dashboard for the Alpaca API Algo Trading Platform

Trading

A Streamlit Dashboard for the Alpaca API Algo Trading Platform

The Alpaca brokerage service is very useful for algorithmic traders that comes with an API to retrieve data and execute trades in...

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How to Analyze Volume Profiles With Python

Stock Analysis in Python

Stock Analysis in Python

Coding a Python Stock Trading bot with Alpaca

Coding a Python Stock Trading bot with Alpaca

Lessons learned building an ML trading system that turned $5k into $200k

Lessons learned building an ML trading system that turned $5k into $200k

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Algorithmic Trading Using Logistic Regression

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PyKrylov: Accelerating Machine Learning Research at eBay

Using Convolutional Neural Networks to Classify Street Signs

End to End Machine Learning: From Data Collection to Deployment

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Data Science

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Automate Your Machine Learning with PostgresML

8 Visualizations with Python to Handle Multiple Time-Series Data

Data Science

8 Visualizations with Python to Handle Multiple Time-Series Data

Data Science

Economic Data Analysis Project with Python Pandas – Data scraping, cleaning and exploration

Learning Natural Language Processing (NLP) Made Easy

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Learning Natural Language Processing (NLP) Made Easy

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Efficient Pandas DataFrames in Python

How we parallelized 600+ pandas functions with Modin

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How we parallelized 600+ pandas functions with Modin

Financial market data analysis with pandas

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Financial market data analysis with pandas

Scaling SHAP Calculations With PySpark and Pandas UDF

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Scaling SHAP Calculations With PySpark and Pandas UDF

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Python Books

Automate Your Machine Learning with PostgresML

Algorithmic Trading with Python: Quantitative Methods and Strategy Development

Automate Your Machine Learning with PostgresML

Algorithmic Trading with Interactive Brokers (Python and C++)

Automate Your Machine Learning with PostgresML

Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis

Automate Your Machine Learning with PostgresML

Python for Algorithmic Trading: From Idea to Cloud Deployment

Automate Your Machine Learning with PostgresML

Algorithmic Short Selling with Python: Refine your algorithmic trading edge, consistently generate investment ideas, and build a robust long/short product

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Get The 46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python

What You Get:

46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python PDF
Jupyter Notebook with working code
Cached options data

Python libraries used:

Pandas, NumPy, SciPy, Matplotlib, Jupyter Notebook.

What you'll learn:

How to calculate and plot options payoffs, code the Black-Scholes pricing formula and greeks, compute implied volatility, analyze implied volatility and more…