Tag: trading indicators
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A Market-Neutral Strategy
The work aims to solve the problem of Markowitz portfolio optimization for a one-year investment horizon through the pairs trading cointegrated strategy. Market-neutral trading strategies seek to generate returns independent of market swings to achieve a zero beta against its relevant market index. Statistical arbitrage (SA), pairs trading, and APO signals are analyzed. The study…
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Plotly Dash TA Stock Market App
The post explains how to deploy a Plotly Dash stock market app in Python with the dashboard of user-defined stock prices. This includes technical indicators like volume, MACD, and stochastic. The steps include selecting a stock ticker symbol (NVDA), retrieving stock data from yfinance API, adding Moving Averages, saving the stock chart in HTML form,…
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Dividend-NG-BTC Diversify Big Tech
SEO Title: Can Dividends, Natural Gas and Crypto Diversify Big Techs? Ultimately, we need to answer the following fundamental question: Can Dividend Kings, NGUSD and BTC-USD Diversify Growth Tech assets? Dividends are very popular among investors, especially those who want a steady stream of income from their investments. Some companies choose to share their profits…
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NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading
The study presents a machine learning-focused analytical approach to optimize NVIDIA’s stock performance using moving average crossovers and aims at comparing the outcomes with simple RNN mean reversal trading strategies. The steps taken involve preparing the stock data, calculating moving averages and drawdowns, plotting heatmaps of returns and drawdowns, and predicting returns and cumulative returns…
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IQR-Based Log Price Volatility Ranking of Top 19 Blue Chips
The focus is on risk assessment of top blue chips. We determine market regimes using standard deviation (STD) of log-domain stock prices.
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Blue-Chip Stock Portfolios for Quant Traders
This post delves into optimizing blue-chip stock portfolios using Python fintech libraries for private DIY self-traders. It includes steps for examining trading signals, comparing stock returns, performing analyses, and implementing forecast models. The content covers AAPL trading signals, risk vs. ROI analysis, a 4-stock portfolio, Monte-Carlo predictions, SPY return/volatility, and SPY Prophet forecast. The examples…
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Working with FRED API in Python: U.S. Recession Forecast & Beyond
The FRED API, or Federal Reserve Economic Data, provides over 267,000 economic time series from 80 sources, offering a wealth of data to promote economic education and research. It encompasses U.S. economic and financial data, including interest rates, monetary indicators, exchange rates, and regional economic data. Additionally, we analyzed correlations, trained currency exchange prediction models,…
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Data Visualization in Python – 1. Stock Technical Indicators
Featured Photo by Monstera on Pexels. In this project, we will implement the following Technical Indicators in Python: Conventionally, we will look at the following three main groups of technical indicators: Input Stock Data Let’s set the working directory VIZ import osos.chdir(‘VIZ’)os. getcwd() and import the key libraries import datetime as dtimport pandas as pdimport…
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Gold Price Linear Regression
This content focuses on predicting gold prices using machine learning algorithms in Python. With an 80% R2-score and a Sharpe ratio of 2.33, it suggests a potential 8% revenue from an investment starting in December 2022. The forecasted next-day price for SPDR Gold Trust Shares is $185.136, aligning with Barchart’s “100% BUY” signal.
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JPM Breakouts: Auto ARIMA, FFT, LSTM & Stock Indicators
The post discusses predicting JPM stock prices for 2022-2023 using several predictive models like ARIMA, FFT, LSTM, and Technical Trading Indicators (TTIs) such as EMA, RSI, OBV, and MCAD. The ARIMA model used historical data, while the partial spectral decompositions of stock prices served as features for the FFT model. TTIs were calculated to validate…
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Post-SVB Risk Aware Investing
The recent collapse of Silicon Valley Bank and its repercussions have prompted a reevaluation of risk-aware investing in the US financial sector. The crisis has exposed the vulnerability of banks invested in long-term fixed income assets, highlighting the importance of diversification and risk management. Market indicators suggest continued volatility and uncertainty, urging investors to exercise…
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Donchian Channel Trading Systems
This article explores the application of algo trading using Python for Altria Group, Inc., a Dividend King diversifying beyond smoking. The historical data for Altria is used to test and contrast trading strategies based on the Donchian Channel indicator. The key is to compare the highest total return when using the Donchian Channel Breakout versus…
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Comparison of Global Growth Stocks – 2. AZN
Summary: A comprehensive QC assessment of top growth stocks in Q1’23 was conducted, focusing on A-rated AstraZeneca PLC (AZN) in the biopharmaceutical industry. The company’s financial indicators, technical analysis, and algorithmic trading signals were analyzed. Backtesting showed a 34% profit from investing in AZN, outperforming the benchmark by 50%.
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Comparison of U.S. Growth Stocks – 1. WMT
The U.S. labor market and consumer spending are robust despite economic challenges. Bank of America reports a 5.1% rise in credit and debit card spending in January. The focus is on A-rated growth stocks like Walmart Inc. (WMT), with promising metrics and technical indicators supporting a Strong Buy sentiment. Algo trading with DI shows successful…
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Biotech Genmab Hold Alert via Fibonacci Retracement Trading Simulations
SeekingAlpha: A ‘Strong Buy’ Call on Genmab. While analysts were issuing bearish calls on Danish biotech Genmab (GMAB), Seeking Alpha contributor Biologics had different thoughts. In fact, Genmab has risen about 6% for 2022 even as the broader biotech sector has shed about 27% for the year. Let’s examine the Genmab stock in terms of…
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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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The $ASML Trading Strategies via the Plotly Stock Market Dashboard
Dr. Dividend has shared an insight into a stock market analysis using Plotly’s interactive Stock Market Dashboard. The rundown explains how to fetch live data using yfinance API, create visuals incorporating moving averages, and craft multiple trading signals, including the use of the MACD and Stochastic Oscillator. The tutorial also guides on saving the final…
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A TradeSanta’s Quick Guide to Best Swing Trading Indicators
This post was motivated by the recent TradeSanta’s insights into Top 6 Indicators For Swing Trading. Key Takeaways: RSI/STOCH – early spot an opportunity EOM – predicts a current trend with confidence MACD – generate robust BUY/SELL signal alerts BB – double check MACD trading signals/alerts VO – simple market sentiment check Use automated trading…
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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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