How Python can analyse and interpret open interest data

IB Broker - Johnsen Cointegration Test Implementation in Python Time series data is a unique and invaluable form of data that captures information over a continuous period. It's used in various fields, from finance to economics, to understand and predict trends, patterns, and behaviours. Among the essential tools for analysing time series data is the Johansen Cointegration Test, which plays a pivotal role in understanding relationships between variables. This below blog aims to provide a comprehensive and beginner-friendly guide to mastering the Johansen Cointegration Test using Python. IBKR Link: Johansen Test https://lnkd.in/eAkQu5b3 #quant #finance #trading #ibbroker #ibkr #johansen #cointegration #test #python

Dynamic Price Modeling with Bates Model in Python 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/eZ9JR9Uv Dynamic price modeling is a technique used to forecast and analyze the pricing behavior of assets, such as stocks, commodities, and currencies. In this video, we will explore the use of the Bates model, a popular statistical model for modeling and simulating stock price processes. The Bates model takes into account the stochastic nature of stock prices and can be used to simulate a wide range of scenarios, including crashes and surges in the market. In this video, we will demonstrate how to implement the Bates model using Python and how to use it to generate dynamic prices. We will also explore the advantages and limitations of the Bates model and discuss its applications in finance and economics. Understanding the intricacies of dynamic price modeling and the Bates model can be beneficial for traders, portfolio managers, and anyone involved in the financial industry. As with any advanced statistical model, it is essential to have a solid understanding of the underlying concepts and techniques. Some suggestions to reinforce your study on this topic include: - Familiarize yourself with the basics of finance and economics - Review the concept of stochastic processes and how they are used in finance - Practice implementing the Bates model using Python - Read up on more advanced topics, such as quantitative finance and computational finance Additional Resources: **Hashtags** #stem #mathematics #finance #economics #pythonprogramming #dynamicpricemodeling #batesmodel #stem #mathematics #finance #economics #pythonprogramming #dynamicpricemodeling #batesmodel Find this and all other slideshows for free on our website: https://lnkd.in/eZ9JR9Uv #stem #mathematics #finance #economics #pythonprogramming #dynamicpricemodeling #batesmodel https://lnkd.in/e6pEGyhs

How to Analyze Volume Profiles With Python It's important to identify value areas to inform trading decisions. One way to do this is by looking at the volume profile. Use Python to view the distribution of volume over a period of time. Get it here: https://lnkd.in/gjgsxVYG

✍ a new blog post: Covered the fundamentals of candlesticks, and their types, and demonstrated how to plot and detect them using Python. #trading hashtag #python hashtag #algotrading https://lnkd.in/dWye2Fgk

Performance Dashboard The main objective of the Signal2Noise site is to provide a way for traders and investors to consume as much relevant information as possible in a snapshot. The first thing you need is a table that produces returns over different time periods and presents them in a way that visually highlights a price move that is not a normal move. #dashboard #python #pythoncode #quantitativefinance

Python for Finance - The below article outlines how to create the initial stages of a python backtesting mean reversion script. Steps 1) Define our symbol pair and download the relevant price data. 2) Plot the two ETF price series against each other to get a visual representation, then run a Seaborn “jointplot” to analyse the strength of correlation between the two series. 3) Run an Ordinary Least Squares regression on the closing prices to calculate a hedge ratio. Use the hedge ratio to generate the spread between the two prices, and then plot this to see if it looks in any way mean reverting. 4) Run an Augmented Dickey Fuller test on the spread to confirm statistically whether the series is mean reverting or not. Reconfirm by calculating the Hurst exponent of the spread series. 5) Calculate the half-life of mean reversion. link: Python for Finance https://lnkd.in/eXp7wFx5 #quant #finance #backtesting #python #meanreversion #adf #hurstexponent #halflife #correlation #pairs #statistical #arbitrage

I've crafted extensive Python automations for dissecting and predicting trends in the Indian stock market. Today, I unveil one of my powerful tools for delving into historical stock data analysis. Analyzing Indian Stock Market: Prices, Trends and Patterns using Python

Excited to share my latest project analyzing the Micro E-mini NASDAQ-100 Futures (MNQ) using Python! #DataScience #QuantitativeFinance I developed a comprehensive analysis toolkit that leverages machine learning to understand intraday trading patterns and predict future price movements while analyzing the daily open range. Key Features: Implemented advanced data processing using yfinance to analyze intraday and historical price data with 15-minute granularity Developed custom volatility metrics focusing on opening range analysis and day range patterns Built predictive models using scikit-learn for forecasting closing prices 📈 Key Findings: Discovered that 33% of trading instances show significant early-session volatility (>35% of daily range) Identified optimal trading windows within the first 45 minutes of market open Uncovered correlation between negative trading days and increased intraday volatility 🛠️ Tech Stack: Python | pandas | seaborn | scikit-learn | matplotlib | numpy | yfinance Check out the full project on GitHub: @guzmanwolfrank https://lnkd.in/eGVHxMPS Python Program: MNQRange #Python #Trading #FinancialMarkets #MachineLearning #DataAnalysis #TechnicalAnalysis #QuantitativeTrading Would love to connect with fellow quants and data scientists interested in financial markets!

Did you know that the Python 𝐑𝐨𝐮𝐧𝐝 function rounds 6.5 to 6 and 7.5 to 8? 🤔 And no its not a bug! At first glance, it seems inconsistent. Shouldn’t both numbers either round up or down? Here’s why: Python uses “𝗿𝗼𝘂𝗻𝗱 𝗵𝗮𝗹𝗳 𝘁𝗼 𝗲𝘃𝗲𝗻”, also known as 𝗯𝗮𝗻𝗸𝗲𝗿𝘀’ 𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴. When a number is exactly halfway between two integers, it ROUNDS TO NEAREST EVEN NUMBER. But why does it matter? ➡️ In finance, statistics, and other fields, small rounding errors can add up to big differences. This is called the "Rounding Bias" ➡️ Historically, bank’s always rounded interest payments up, slightly overpaying customers. Small errors on millions of transactions led to huge financial losses over time. ➡️ Hence this method was designed for fairness and accuracy, especially in large datasets. 💡So the next time you see this behavior, you know it’s not a bug—it’s math in action! #datascience #python #dataengineering #analytics

Efficient Market Hypothesis with Python 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dYEsiag9 The Efficient Market Hypothesis (EMH) posits that financial markets reflect all available information, making it impossible to achieve consistent returns above the market rate without taking on excessive risk. In this video, we will explore how to implement EMH using Python, a popular programming language for data analysis and visualization. The EMH has three forms: the semi-strong form, which assumes that prices reflect all publicly available information, the strong form, which assumes that prices reflect all publicly available information, including insider information, and the weak form, which assumes that prices reflect all historical market data. We will use Python to demonstrate how to test the EMH using empirical data and evaluate its validity. The key to implementing EMH with Python is to use statistical methods, such as regression analysis, to analyze the relationship between stock prices and various financial indicators. We will also use Python's popular data manipulation package, Pandas, to manipulate and clean our data. By the end of this video, viewers will have a thorough understanding of the EMH and how to implement it using Python. The efficiency of financial markets is essential for making informed investment decisions. By understanding the EMH and its implications for investing, viewers will gain a valuable insight into the world of finance. Additional Resources: You can explore more resources on the Efficient Market Hypothesis, including academic papers and online courses, at your local library or online. #stem #financialmarkets #python #dataanalysis #modeling #statistics #economy #investing Find this and all other slideshows for free on our website: https://lnkd.in/dYEsiag9 #stem #financialmarkets #python #dataanalysis #modeling #statistics #economy #investing https://lnkd.in/dk3a8cnX