Category: Predictions
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The Qullamaggie’s TSLA Breakouts for Swing Traders
The content explains a Python-based stock scanner project that analyses TSLA’s historical data downloaded from Yahoo Finance. It applies a set of functions to identify stocks meeting growth criteria and checks for consolidation. The output is a series of plots showing original close price vs filtered data or breakouts. The scanner aims to help swing…
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SeekingAlpha Risk/Reward July Rundown
Lets see what SA is up to in terms of Risk/Reward as of 2022-07-25: Cryptocurrency Digest: SA Morning Briefing: SPY: Overbought Demand Testing Resistance (Technical Analysis) Wall Street Breakfast: In arguably the most important week for Wall Street this summer, with the Fed decision and GDP on tap, earnings could actually end up determining direction. There are 175…
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Macroaxis AI Investment Opportunity
The research presented examines the portfolio optimization potential of Macroaxis’ new artificial intelligence (AI) system. This tool automates processes like asset allocation, portfolio diversification and rebalancing, and equity research, providing a list of suggested investments. The AI makes suggestions based on market conditions and investor risk tolerance, and has demonstrated higher average returns than the…
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Algorithmic Testing Stock Portfolios to Optimize the Risk/Reward Ratio
Investors can optimize their stock portfolio by invoking backtesting within the realm of algorithmic trading. The goal is to optimize the specific portfolio by maximizing returns and the Sharpe ratio.
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Quant Trading using Monte Carlo Predictions and 62 AI-Assisted Trading Technical Indicators (TTI)
Algorithmic Trading using Monte Carlo Predictions and 62 AI-Assisted Trading Technical Indicators (TTI): The goal of the here presented pilot study is to develop and test an end-to-end Python-3 script in Jupyter that implements algorithmic trading. Thanks to high-level automation and integration of multiple tasks, the script can simultaneously analyze hundreds of technical indicators, run…
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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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Bear Market Similarity Analysis using Nasdaq 100 Index Data
We calculate similarities between the current market conditions and the selected six historical bear market events using the Nasdaq 100 Index Data. Results suggest that Covid19 pandemic, 1987 black Monday, and 1990 recession are closest to the current bear market.
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Basic Stock Price Analysis in Python
Our basic stock price analysis in Python includes stock prices, stock volume, market capitalization, 50/200-day moving average, scattered X-plot matrix, and stock volatility or standard deviation.
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Track All Markets with TradingView
Track All Markets with TradingView: getting started tips teaching a new dog old tricks making the most of your market analysis Global screener new features and more
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S&P 500 Algorithmic Trading with FBProphet
We use Facebook’s Prophet to forecast S&P 500 stock adjusted close price. We plot the results simulating an initial investment of $1,000.
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Predicting Trend Reversal in Algorithmic Trading using Stochastic Oscillator in Python
This is the example stochastic oscillator in Python for algorithmic trading $NVIDIA candlestick chart vs a stochastic oscillator chart over our trading period.
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Stock Forecasting with FBProphet
Prophet from Meta (Facebook) is a procedure for forecasting time series data such as stocks. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
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Inflation-Resistant Stocks to Buy
Inflation-Resistant Stocks to Buy AAPL Example Python workflow Download 3 historical datasets – stock price and monthly/annual CPI Compute the monthly/annual stock performance (%) and CPI rate (%) Apply linear regression to the stock vs CPI performance cross-plot Check the slope or gradient of the linear trend – positive, negative or zero.
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A Weekday Market Research Update
Market technical analysis, research and ideas.
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Short-Term Stock Market Price Prediction using Deep Learning Models
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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
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Keras LSTM Stock Prediction
Despite the vast amount of historical stock data, the high noise to signal ratio and varied market conditions cause inadequate stock price predictions. A solution to these challenges is Long Short Term Memory (LSTM). LSTMs offer a range of adjustable parameters without the necessity for fine-tuning, and efficiently handle sequential data.
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