Tag: finance
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Predicting the JPM Stock Price and Breakouts with Auto ARIMA, FFT, LSTM and Technical Trading Indicators
Featured Photo by Pixabay In this post, we will look at the JPM stock price and relevant breakout strategies for 2022-23. Referring to the previous case study, our goal is to combine the Auto ARIMA, FFT, LSTM models and Technical Trading Indicators (TTIs) into a single framework to optimize advantages of each. Specifically, we will…
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Risk-Return Analysis and LSTM Price Predictions of 4 Major Tech Stocks in 2023
The open-source Python workflow breaks down our investigation into the following 4 steps: (1) invoke yfinance to import real-time stock information into a Pandas dataframe; (2) visualize different dataframe columns with Seaborn and Matplotlib; (3) compare stock risk/return using historical data; (4) predict stock prices in 2023 with the trained LSTM model. Input Data Let’s…
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XOM SMA-EMA-RSI Golden Crosses ’22
Featured Photo by Johannes Plenio on Pexels. Today we will discuss the XOM stock using most basic technical trading indicators (TTIs) within the Python library ta-lib. Recall that this library is widely used by algo traders requiring to perform technical analysis of financial market data. It includes 150+ indicators such as ADX, MACD, RSI, Stochastic,…
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Cloud-Native Tech Autumn 2022 Fair
Let’s dive deeper into the cloud-native tech trends and features to follow in Q4 2022 and beyond. Contents: Markets Services Serverless Cybersecurity DevSecOps ML/AI/IoT Use-Cases Events Training Explore More Infographic
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Are Blue-Chips Perfect for This Bear Market?
12 High-Yield Blue-Chips That Are Perfect For This Bear Market TradingView Technical Analysis charts, trends, forecasts, oscillators, bias, volatility, risk management
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OXY Stock Technical Analysis 17 May 2022
OXY Stock Technical Analysis 17 May 2022 Occidental Petroleum Is About To Crush Its Debt And Own The House – debt load to fall by nearly $20B in the coming year.
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ML/AI Regression for Stock Prediction – AAPL Use Case
1. Install Yahoo finance library 2. Call all dependencies that we will use for this exercise 3. Define the ticker you will use 4. Let’s look at the data table 5. Data Exploration Phase 6. Data Preparation, Pre-Processing & Manipulation 7. Apply Linear Regression 8. Perform ML QC Analysis 9. Final Output