Quant Science

Stock Prediction AI: Using Machine Learning and Deep Learning to predict stock price movements in Python. The Python code is 100% free on GitHub. Let's dive in (bookmark this): 1. The Python Machine Learning and Deep Learning Libraries: - mxnet - gluon - sklearn - xgboost 2. Stock Price Data (Train/Test) The dashed vertical line represents the separation between training and test data. GS is shown but will use 72 assets. Daily prices for each asset. 3. Technical Indicators Analysis uses: - Moving Averages - MACD - Bollinger Bands - Momentum 4. Sentiment News The analysis uses a pretrained BERT model to classify news articles as positive or negative sentiment. 5. Fourier Transforms Used to extract global and local trends (and to de-noise) 6. ARIMA ARIMA is a forecasting method that uses lagged regression and autocorrelation. Analysis uses to extract new features and denoise. 7. XGBoost Feature Importance The analysis uses an xgboost model to create feature importances. 8. Generative Adversarial Network (GAN) The future pattern of GS's stock should be more or less the same (unless something drastically changes). GAN will allow us to generate data in the future with a similar distribution to historical data. Get the complete code here: https://lnkd.in/dWDr8TrT Want to learn how to get started with algorithmic trading with Python? Then join us on March 26th for a live webinar, how to Build Algorithmic Trading Strategies (that actually get results) Register here (500+ registered): https://lnkd.in/gysn5qza

Free Python code from the book: All the source code that goes with the book is on GitHub. Get it here: https://lnkd.in/d4sz7nm Want to learn algorithmic trading for investment growth in Python? Join us on May 26th for a live training (500 seats): https://lnkd.in/eX837hZe

Why python is insane for algorithmic trading: 1. Visualization: Plotly ($0) 2. Data analysis: Pandas ($0) 3. Market Data: OpenBB ($0) 4. Technical indicators: TA-lib ($0) 5. Machine Learning: Scikit Learn ($0) Total cost: $0 Want to learn how to get started with algorithmic trading in Python (with trading strategies that actually work)? ?? Join live us on March 26th: https://lnkd.in/gysn5qza

JP Morgan's Python training. Available 100% for free: Here's the link on GitHub: https://lnkd.in/d_7HdaY Want to learn how to get started with algorithmic trading with Python? Then join us on March 26th for a live webinar, how to Build Algorithmic Trading Strategies (that actually get results) Register here (780+ registered): https://lnkd.in/eX837hZe

Time Series Momentum. A 23-page PDF. Here are the best parts: Time series momentum (page 6) Regression analysis and trading strategies. t-statistic by asset class (page 7) t-statistic by holding period (page 8) Time series momentum factor (page 9) Performance momentum vs passive (page 12) === I have 1 more thing for you. If you want to get started with algorithmic trading in Python, I have a free workshop on March 26th. I'll cover my best strategies and have a full Python code demo. Register here (500 seats): https://lnkd.in/gysn5qza

How to make a simple algorithmic trading strategy with a 472% return using Python. A thread. ?? This strategy takes advantage of "flow effects", which is how certain points in time influence the value of an asset. This strategy uses a simple temporal shift to determine when trades should exit relative to their entry for monthly boundary conditions. The signals for when to go short, when to cover shorts, when to go long, and when to close longs are all linked to these recurring monthly cycles. This periodic "flow" of signals—month-in, month-out—creates a systematic pattern. 1. Load the libraries and data Import these libraries. Then run the code to ingest price data on TLT. 2. Generate Signals We'll first set up an empty data frame to track the long/short signals. Then we create short entry signals on each new month's 1st and 5th day. Similarly, we make long signals 7 days and 1 day before the end of each month. 3. Run this code to get the Trade PnL Use vbt.Portfolio.from_signals(). Use pf.stats() to return the portfolio stats summary tear sheet. I have 1 more thing for you. If you want to get started with algorithmic trading in Python, I have a free workshop on March 26th. I'll cover my best strategies and have a full Python code demo. Register here (500 seats): https://lnkd.in/gysn5qza

?? BREAKING: New Python Library for Finance Analysis with AI Agents Here's a 30-second overview (+ Python Getting Started Tutorial): 1. OpenBB LLM Agents A Python library for creating financial analyst agents. - Financial Research - Answer questions with up-to-date financial data - Function calling to use OpenBB Platform 2. Installation: pip install openbb-agents --upgrade 3. Setup You can use it with an OpenAI API Key 4. Basic Querying with AI Agents Use openbb_agent() to create an agent that can answer questions like, "What's the current stock price of TSLA?" 5. Function Calling with OpenBB Tools You can add tools like access to OpenBB's Equity Fundamental Income Statement Data. Check out the getting started tutorial here: https://lnkd.in/efGuYE6M === I have 1 more thing for you. If you want to get started with algorithmic trading in Python, I have a free workshop on March 26th. I'll cover my best strategies and have a full Python code demo. Register here (500 seats): https://lnkd.in/gysn5qza

How to do financial data analysis using ChatGPT A 54-page PDF. OpenAI's flagship model, ChatGPT-4o offers enhanced natural language understanding and more coherent responses. This paper investigates ChatGPT-4o's capabilities in financial data analysis, including zero-shot prompting, time series analysis, risk and return analysis, and ARMA-GARCH estimation. Get the paper (54 page PDF): https://lnkd.in/e48rZbPq === I have 1 more thing for you. If you want to get started with algorithmic trading in Python, I have a free workshop on March 26th. I'll cover my best strategies and have a full Python code demo. Register here (500 seats): https://lnkd.in/gysn5qza

Every trader should know Python. How to do this in 1 line of code: The Python library is called quantstats. Here's some cool stuff you can do with 1 line of Python code. 1. First, run this: pip install quantstats 2. Visualize stock performance Use qs.plot.snapshot() 3. Create a Strategy Tearsheet Use qs.reports.html() === I have 1 more thing for you. If you want to get started with algorithmic trading in Python, I have a free workshop on March 26th. I'll cover my best strategies and have a full Python code demo. Register here (500 seats): https://lnkd.in/gysn5qza

Over the past 10 years, I've watched over 250 YouTube videos on quantitative finance and algorithmic trading in Python. And the truth is, 99% of them were a complete waste of time. But these 8 are worth more than a 4-year degree: 1. Algorithmic Trading Using Python (4.5 hours) Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions. https://lnkd.in/eScMTHm 2. Quantitative Stock Price Analysis with Python (25 minutes) We look at some quantitative analytical methods of stock price changes using Python and pandas. https://lnkd.in/g4R36kdu 3. How to Create & Test Trading Algorithm in Python (20 minutes) Learn how to download and manipulate data then develop a momentum strategy trading algorithm with Python. https://lnkd.in/gqH_6UXs 4. Python for Quant Finance (50 minutes) The talk discusses and illustrates why Python might be the right choice for implementing ambitious quant finance applications and projects. https://lnkd.in/gb_TPE9r 5. Algorithmic Trading in Python (3 hours) The video is a full tutorial that starts from basic installation of Python and anaconda all the way to backtesting strategies and creating trading API. https://lnkd.in/ggDt4teE 6. How to Code a Trading Bot in Python (20 minutes) In this video, we are going to code a Python trading algorithm in the QuantConnect platform. https://lnkd.in/gEiSUpGG 7. Stock Price Prediction Using Python & Machine Learning (50 minutes) In this video, you will learn how to create an artificial neural network called Long Short Term Memory LSTM to predict the future price of a stock. https://lnkd.in/gzEeJk4v 8. Estimating a Risk Factor Model for a Stock with Live Data (60 minutes) In this tutorial, we will learn how to estimate the Fama French Carhart four-factor risk model exposures for an arbitrary stock using live data in Python. https://lnkd.in/gJbjPYDA I have 1 more thing for you. If you want to get started with algorithmic trading in Python, I have a free workshop on March 26th. I'll cover my best strategies and have a full Python code demo. Register here (500 seats): https://lnkd.in/gysn5qza