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1 week ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
Exploratory data analysis is a great way of understanding and analyzing the data sets. The EDA technique is extensively used by data scientists and data analysts to summarize the main characteristics of data sets and to visualize them through different graphs and plots. It helps data scientists to search for patterns, spot anomalies, or check assumptions. It helps to determine if the statistical techniques that you are using for data analysis are either appropriate or not.
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1 week ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
Time series analysis of data is not just a collection of numbers, in this case Netflix stocks. It is a captivating tapestry that weaves together the intricate story of our world with Pandas. Like a mystical thread, it captures the ebb and flow of events, the rise and fall of trends, and the emergence of patterns. It reveals the hidden connections and correlations that shape our reality, painting a vivid picture of the past and offering glimpses into the future.
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3 weeks ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
This is a portion of some of my recent inflation DD from researching Michael Burry/Scion’s recent 13F plays. I thought it was an interesting investing strategy and wanted to share it here in case anyone is interested. There’s more research over on r/Burryology (specifically this post).
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1 month ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
Backtesting a Basic Breakout Trading Strategy With PythonTraditional algorithmic trading strategies, such as the SMA crossover and RSI threshold breakout, have become increasingly ineffective due to their simplicity and the sheer number of traders using them. As a result, these strategies often fail to provide a competitive edge in today’s markets. Instead of relying on these outdated approaches, it’s time to explore something innovative.
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1 month ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
Algorithmic trading is a widely adopted trading strategy that has revolutionized the way people trade stocks. More and more people are making money on the side by investing in stocks and automating their trading strategies. This tutorial will teach you how to build stock trading algorithms using primitive technical indicators like MACD, SMA, EMA, etc., and select the best strategies based on their actual performance/returns, completely using Python.
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1 month ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
This project combines a Python framework, with tools like Matplotlib, Sklearn, and Yahoo Finance to predict future stock prices. I used a Linear Regression Model from Sklearn because it’s well-suited for forecasting stock trends. To build an accurate model, I pulled a real-time dataset from Yahoo Finance, which provided essential features like “Close Price,” “Open Price,” “Volume,” “High,” and “Low.” I also added a “Date” feature to keep track of daily data.
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1 month ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
In the field of financial analysis, the capacity to forecast future market movements and behaviors is crucial for making informed decisions. Within this context, LSTM (Long Short-Term Memory) models have become valuable tools for predicting financial time series. This article delves into how LSTM models function and their use in forecasting stock prices and other financial metrics.
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1 month ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
Statistical Arbitrage (Stat Arb) encompasses trading strategies that typically capitalize on either mean reversion in stock prices or opportunities arising from market microstructure anomalies. This approach is highly quantitative and analytical, often involving a broadly diversified portfolio of securities. However, to demonstrate the concept, let’s consider an example involving pairs trading with only two cointegrated securities.
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1 month ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
We have all heard it! — “Time in the market beats timing the market”At the same time, we are all to some extent guilty of trying to time the market. The market always seems to break some new all-time high records, so we wait for the inevitable crash/pullback to invest. It’s high time we put both strategies to test. Basically, what I wanted to analyze wasWhether waiting for a crash to invest is a better investment strategy than staying invested?
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2 months ago | 
medium.datadriveninvestor.com | Ayrat Murtazin
Disclaimer: I am not a financial advisor. This entire post represents my personal views and opinions, and should not be taken as financial advice (or advice of any kind whatsoever). I encourage you to do your own research, take anything I write with a grain of salt, and hold me accountable for any mistakes you may catch. Also, full disclosure, I hold a net long position in GME, but my cost basis is very low, and I’m using money I can absolutely lose.
